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Sales Scenario Analysis

Connect ordinary worksheet data to a reactive Python notebook with pandas, explore pricing scenarios using interactive controls, and publish calculated summary tables back to Excel.

How to interact with this live demo
  1. Edit the spreadsheet: Change any number in the blue worksheet cells (A5:C9 — Units or Price).
  2. Move the slider: Drag the Discount scenario (%) slider in the Python notebook on the right.
  3. Observe the flow: Notice how the Matplotlib chart re-renders immediately and the green worksheet summary (BF.OUTPUT("summary")) updates automatically.

Need more space? Click Open in separate tab on the workbook header to test in a dedicated browser window.

Live interactive workbook​

Sales Scenario Analysis interactive Python workbook
Loading Python workbook…

How the notebook architecture works​

This starter example demonstrates the core four-step lifecycle of Boardflare Python for Excel:

1. Excel remains the source of truth​

The workbook retains the durable operational data in cells A5:C9. The notebook connects to this range with:

inputs = bf.inputs(sales=bf.ref("A5:C9", headers=True))

Whenever you edit values in the worksheet, Boardflare pushes the updated range directly into the marimo dependency graph as a pandas DataFrame.

2. Reactive calculation in normal Python​

The analysis runs in a marimo reactive notebook cell:

analysis = inputs["sales"].copy()
analysis["Base revenue"] = analysis["Units"] * analysis["Price ($)"]
analysis["Scenario revenue"] = analysis["Base revenue"] * (1 - float(discount.value) / 100)

The discount slider is a notebook UI control (mo.ui.slider). Adjusting the slider recomputes dependent cells without altering the underlying worksheet assumptions.

3. Publishing results back to Excel​

The calculated scenario metrics are published back to the workbook:

bf.publish(outputs={"summary": summary})

In Excel, cell E5 displays this live result using the formula:

=BF.OUTPUT("summary")

Notebook charts and detailed diagnostics stay inside the notebook, while Excel receives only the clean, published summary table.

What to try next​

  • Explore Demand & Inventory Planner for multi-sheet modeling, constrained budget optimization, and custom BF.FUNCTION() formulas.
  • Explore Curve Fitting for scientific regression, SciPy parameter estimation, confidence bands, and residual diagnostics.
  • Ready to use notebooks in your own spreadsheets? Follow the Getting started guide.