Extracting Power BI Dashboard Metadata by Deconstructing PBIX Files with Python
This article explains how to unpack Power BI .PBIX files, parse the layout JSON to retrieve widget positions, used fields, and settings, and then use Python and Pandas to generate a concise data frame of all unique fields, ultimately building a Flask‑based web tool for easy visualization.
Many data‑analysis enthusiasts use Power BI to build dashboards, but repeated adding and removing of widgets can leave the underlying data structure chaotic.
The only way to preview the entire dashboard in Power BI is to manually browse each widget and note the used fields, which is tedious.
The proposed solution starts by treating the .PBIX file as a regular archive (e.g., using WinRAR) and extracting its contents. Inside the extracted folder, the "layout" folder under "Report" contains JSON, forms, and dictionaries that describe every widget’s size, coordinates, used field names, settings, titles, and more.
A Python script parses this JSON layout, extracts each widget’s associated tables and fields, and then uses Pandas to consolidate the information into a data frame, removing duplicate entries (e.g., shared widget information).
The resulting data frame, displayed in a Jupyter Notebook, lists all unique data fields used across the dashboard.
To make the process more user‑friendly, the script can be wrapped in a Flask web application, allowing users to paste the layout JSON string into a web form, run the extraction, and view the results as an HTML table.
The tool is free, ad‑free, and can be deployed on platforms like Heroku, though performance may vary.
Future work includes tracking changes, automatically generating reports, and expanding the script to manipulate other files within the .PBIX archive, enabling further customization of Power BI dashboards via Python.
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