powerbi-python-visuals

Python Visuals in Power BI (PBIR)

Report modification requires tooling. Two paths exist:

1. pbir CLI (preferred) -- use the pbir command and the pbir-cli skill. Install with uv tool install pbir-cli or pip install pbir-cli. Check availability with pbir --version.
2. Direct JSON modification -- if pbir is not available, use the pbir-format skill (pbip plugin) for PBIR JSON structure and patterns. Validate every change with jq empty <file.json>.

If neither the pbir-cli skill nor the pbir-format skill is loaded, ask the user to install the appropriate plugin before proceeding with report modifications.

Python visuals execute matplotlib/seaborn scripts to render static PNG images on the Power BI canvas. Prefer seaborn over raw matplotlib for cleaner syntax and better defaults -- it handles most chart types with less code.

Visual Identity

• visualType: pythonVisual
• Data role: Values (columns and measures, multiple allowed)
• Data variable: dataset (pandas DataFrame, auto-injected)
• Row limit: 150,000 rows
• Output: Static PNG at 72 DPI -- no interactivity

Workflow: Creating a Python Visual

Step 1: Add the Visual

Create the visual.json file manually (see pbir-format skill in the pbip plugin for JSON structure) with visualType: pythonVisual, field bindings for the columns and measures you need (use Values:Table.Column or Values:Table.Measure format), and position/size as required.

Step 2: Write the Script

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(8, 4))
ax.bar(dataset["Date"], dataset["Sales"], color="#5B8DBE")
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
plt.tight_layout()
plt.show()  # MANDATORY

Critical rules:

• plt.show() is mandatory as the final line -- nothing renders without it
• dataset is auto-injected as a pandas DataFrame; do not create it
• Column names match the nativeQueryRef (display name) from field bindings
• Only the last plt.show() call renders; multiple figures not supported