/python-visuals

name: python-visuals
description: Python visual creation and matplotlib/seaborn patterns for PBIR reports. Automatically invoke when the user mentions "Python visual", "matplotlib in Power BI", "seaborn in Power BI", "pythonVisual", or asks to "create a Python visual", "add a matplotlib chart", "write a Python visual script".

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/reg