Python Visuals in Power BI (PBIR)

Use pbir for every report mutation. Read PBIR metadata only for diagnosis. If pbir is unavailable or lacks an operation, stop and report the gap; never edit report JSON directly.

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

pbir add visual pythonVisual "Report.Report/Page.Page" --name PythonChart \
  --data "Values:Sales.Date" --data "Values:Sales.Revenue"

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

Step 2b: Review

Before presenting the script to the user, dispatch the python-reviewer agent to validate correctness and provide design feedback.

Step 3: Inject the Script

pbir visuals python "Report.Report/Page.Page/PythonChart.Visual" \
  --script-file chart.py

The CLI handles PBIR string escaping.

Step 4: Validate

pbir visuals bind "Report.Report/Page.Page/PythonChart.Visual" --show
pbir validate "Report.Report" --all

PBIR Format

For read-only diagnosis, scripts are stored in visual.objects.script[0].properties:

{
  "source": {"expr": {"Literal": {"Value": "'import matplotlib.pyplot as plt\\n...\\nplt.show()'"}}},
  "provider": {"expr": {"Literal": {"Value": "'Python'"}}}
}

The CLI handles all escaping automatically.

Supported Libraries

Power BI Service (Python 3.11)

Package	Version	Purpose
matplotlib	3.8.4	Primary plotting
seaborn	0.13.2	Statistical visualization
numpy	2.0.0	Numerical computing
pandas	2.2.2	Data manipulation
scipy	1.13.1	Scientific computing
scikit-learn	1.5.0	Machine learning
statsmodels	0.14.2	Statistical models
pillow	10.4.0	Image processing

Not supported: plotly, bokeh, altair (networking blocked in Service).

Full package list: https://learn.microsoft.com/power-bi/connect-data/service-python-packages-support

Desktop

Any locally installed package works without restriction.

Best Practices

1. Always call plt.show() -- mandatory, must be the final line
2. Use figsize=(w, h) to match container aspect ratio (72 DPI output)
3. Remove chart chrome -- ax.spines["top"].set_visible(False) etc.
4. Use hex colors matching the report theme
5. Keep scripts simple -- 5-min timeout Desktop, 1-min Service
6. Minimize transforms -- do heavy computation in DAX/Power Query instead
7. Use try/except for robustness in production scripts
8. Copy data first -- data = dataset.copy() before manipulation

Limitations

Constraint	Desktop	Service
Output	Static PNG, 72 DPI	Static PNG, 72 DPI
Timeout	5 minutes	1 minute
Row limit	150,000	150,000
Payload	--	30 MB
Networking	Unrestricted	Blocked
Gateway	Personal only	Personal only
Cross-filter FROM	Not supported	Not supported
Receive cross-filter	Yes	Yes
Publish to web	Not supported	Not supported
Embed (app-owns-data)	Not supported	Not supported

Script Structure Template

import matplotlib.pyplot as plt
import numpy as np

# 1. Guard against empty data
if dataset.empty:
    fig, ax = plt.subplots(1, 1, figsize=(6, 4))
    ax.text(0.5, 0.5, "No data available", ha='center', va='center', fontsize=14, color='#888888')
    ax.axis('off')
    plt.show()
else:
    # 2. Data preparation (dataset is auto-injected)
    data = dataset.copy()

    # 3. Create figure with explicit size
    fig, ax = plt.subplots(figsize=(8, 4))

    # 4. Plot
    ax.plot(data["X"], data["Y"], color="#5B8DBE", linewidth=2)

    # 5. Style
    ax.spines["top"].set_visible(False)
    ax.spines["right"].set_visible(False)
    ax.grid(axis="y", alpha=0.3)

    # 6. Layout and render
    plt.tight_layout()
    plt.show()

When to Use a Script Visual

Reach for a Python visual only when all of the following hold:

• The chart has no native equivalent and no reasonable Deneb spec
• The value is in a statistical computation that must run at render time (model fit, kernel density, forecast band), not just a shape Vega could draw
• The visual does not need to be a cross-filter source, hover tooltips, publish-to-web, or app-owns-data embed
• The report is served in a Pro/PPU or higher capacity with a Fabric-enabled region

If interactivity or cross-filtering matters, use Deneb (a static PNG cannot be a selection source). If the need is a small inline mark (sparkline, bar, status pill), use an SVG measure (no row cap, no timeout, no licensing/region gate, renders under publish-to-web). The script visual's niche is narrow: compute-at-render statistical plots for internal or org consumption.

Python vs R once a script visual is the right call: use Python when the computation leans on scikit-learn, statsmodels, or scipy, or when surrounding report logic is already Python. Use R for publication-quality statistical defaults and packages with no Python peer (forecast, corrplot, pheatmap, ridgeline/violin). Where equal, default to whichever language the report's other scripts use; mixing doubles the publish-time package surface to validate.

Do not default to a script visual because a chart type "looks statistical." A box plot, lollipop, or dumbbell is an SVG-measure or Deneb job; reserve scripts for charts that genuinely compute.

References

• references/data-model.md -- dataset grouping mechanic, the row/byte caps, and how to force per-row input
• references/community-examples.md -- seaborn gallery examples organized by chart type, plus matplotlib and Python Graph Gallery links
• references/chart-patterns.md -- Common matplotlib/seaborn chart patterns (bar, heatmap, donut, KPI, area)
• examples/script/ -- Standalone Python scripts (bar-chart, trend-line) -- ready to inject into visual.json after escaping
• examples/visual/bar-chart.json -- PBIR visual.json: horizontal stacked bar with PY comparison lines and % change labels
• examples/visual/kpi-card.json -- PBIR visual.json: text-based KPI with value, % change indicator, and PY comparison
• examples/visual/trend-line.json -- PBIR visual.json: area chart with line plot and monthly x-axis

Fetching Docs

To retrieve current Python visual / package support docs, use microsoft_docs_search + microsoft_docs_fetch (MCP) if available, otherwise mslearn search + mslearn fetch (CLI). Search based on the user's request and run multiple searches as needed to ensure sufficient context before proceeding.

Related Skills

• pbi-report-design -- Layout and design best practices
• r-visuals -- R Script visuals (same concept, different language)
• deneb-visuals -- Vega/Vega-Lite visuals (interactive, vector-based alternative)
• svg-visuals -- SVG via DAX measures (lightweight inline graphics)
• pbir-format (pbip plugin) -- PBIR JSON format reference