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

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

Set the script content in the visual's

objects.script[0].properties.source

literal value (see PBIR Format section below).

Escaping rules for visual.json injection:

The script must be encoded as a single-quoted DAX literal string inside

expr.Literal.Value

:

• Newlines in the script become

  \n

  in the JSON string
• Double quotes inside the script (e.g.,

  "#5B8DBE"

  ) become

  \"

  in the JSON string
• The entire script is wrapped in single quotes:

  'import matplotlib...\nplt.show()'

• See

  examples/visual/

  for a complete real-world visual.json showing this encoding

Step 4: Validate

Validate JSON syntax with

jq empty <visual.json>

and inspect the visual.json to confirm script content and field bindings.

PBIR Format

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)	Ending May 2026	Ending May 2026

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 Python Visuals

Python visuals are appropriate for statistical and analytical visualizations where the focus is on data analysis rather than interactivity. Use Python visuals when you need:

• Statistical charts (distributions, regressions, correlations, violin plots)
• Analytical visualizations leveraging scipy, scikit-learn, or statsmodels
• Chart types that seaborn/matplotlib handle well but Power BI natives don't support

Output is static PNG -- no cross-filtering FROM the visual, no hover/tooltip interactivity. Use Deneb instead for interactive custom visuals. Use SVG measures for simple inline graphics in tables/cards.

References

• 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