Python in Excel: 10,000 What-Ifs

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I offer these live sessions for professionals who are serious about improving their work and investing in their development. I bring my name, experience, and preparation, and I expect you to bring your attention, participation, and a clear learning goal.

• Use your real name and a regular, non-disposable email address that you actively monitor. Temporary or burner email addresses are not accepted. Registrations using them or false identity details may be canceled. A work email is preferred; a regular personal email is welcome.
• Come ready to engage. Set aside the scheduled time, follow the demonstrations, and contribute through relevant questions, chat, or discussion when invited. Bring a practical challenge you want to address and be ready to apply what you learn.
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Live attendance is free. Your commitment to showing up prepared and participating is expected.

Python in Excel: 10,000 what-ifs

Cover tagline: 10,000 what-ifs in one Python cell

Most Excel models ask you to pick a few assumptions and call them scenarios. Best case. Base case. Worst case.

Real life is usually messier than that.

This free live, type-along session shows how Python in Excel lets you run thousands of possible futures, see the range of outcomes, and compare decisions without leaving the workbook.

We'll build one simulation, make it usable from worksheet cells, and use the results to answer a practical question: given all these possible outcomes, what should we actually do?

No Python experience is required. If you've never opened a Python cell before, we'll get you started in the first few minutes.

What we'll cover

We'll work through three examples drawn from my book, Python in Excel for Data Analytics (Packt).

1. 10,000 what-ifs in one cell

We'll start with a profit model where demand is uncertain.

Instead of choosing three hand-picked scenarios, we'll run the model 10,000 times and look at the full range of possible outcomes.

You'll see the distribution in a histogram and answer questions like "How often do we actually lose money?" with a line of code.

2. Python that someone else can actually use

Python gets a lot more useful in Excel when the person using the workbook doesn't have to touch the code.

We'll build a moving average over a noisy daily series, then connect it to a worksheet input so the user can change the window and rerun the analysis directly from Excel.

The Python does the work. The workbook still feels like a workbook.

3. From "what could happen?" to "what should we do?"

A simulation gives you thousands of possible futures. Eventually you still have to make a decision.

We'll use percentiles to turn those outcomes into something useful for planning, then compare two order quantities against the exact same 10,000 futures.

That gives you a much better basis for a decision than comparing two separate collections of guesses.

Why Python in Excel here?

Excel already has Goal Seek, Data Tables and Scenario Manager. They're useful tools, especially when the number of assumptions and scenarios stays manageable.

But real uncertainty doesn't always fit neatly into a few cells labeled best, base and worst.

Python in Excel makes it practical to generate thousands of scenarios, summarize the results and compare decisions, while keeping the model and its inputs inside Excel.

The focus of this session is not memorizing simulation code. It's learning when this kind of analysis is worth reaching for and what decisions it can help you make.

What this session is

✔ Live demonstrations in a real Excel workbook, not a slide deck
✔ Type-along examples using only a few lines of Python at a time
✔ Built for Excel users, with each technique connected to familiar worksheet concepts
✔ Honest about what Python in Excel does well and where it still has limitations
✔ A practical preview of the approach used in my book and longer training

What this session isn't

✖ A Python programming class
✖ A full course on Monte Carlo simulation
✖ A tour of every Python in Excel feature
✖ An AI or Copilot demonstration
✖ A passive "watch someone code for 45 minutes" webinar

Who this is designed for

This session is for people who build, analyze or maintain Excel workbooks as part of their job.

It will be especially useful if you've run into things like:

• decisions built around one "best guess" number
• what-if analysis limited to a few hand-picked scenarios
• models where you'd like to understand the range of possible outcomes, not just one answer
• Python analysis that only the person who wrote the code can operate
• difficulty comparing two decisions under the same assumptions and uncertainty

Typical attendees include business, financial and operations analysts, professionals responsible for planning or forecasting, and Excel power users who want a broader analytical toolkit without abandoning the workbook.

Who this is probably not for

This session may be too introductory if you already build Monte Carlo simulations regularly in Python or another programming language.

There is also no AI or Copilot demo in this session. The focus is simulation, worksheet-driven Python and decision analysis.

And because this is a working session, please register if the topic is genuinely relevant to the work you do or want to do in Excel.

How the live session works

This is a 45-minute live online session.

It is free to attend and designed to be type-along. Every example uses a small amount of code, and we'll build it together inside Excel.

Follow the demonstrations closely and participate through relevant questions or discussion when invited.

After the session, the recording, follow-along workbook and code will be posted free to Python in Excel for Analysts: The Free Sessions on Gumroad, usually within a few days:

https://stringfestdata.gumroad.com/l/pxlaf

Code and supporting files are also available on GitHub:

https://github.com/stringfestdata/python-excel-analysts-free

What you need to type along

You'll need Microsoft 365 with Python in Excel available.

Look on the Formulas tab in Excel for the Python group. If your software setup prevents typing along, follow the demonstrations closely and participate through relevant questions or discussion when invited.

No previous Python experience is required.

The book, and what comes next

Everything in this session comes from Python in Excel for Data Analytics (Packt).

The book goes from your first =PY() cell through exploratory analysis, statistical testing, regression, forecasting, simulation, text analytics and AI-assisted work, with a companion workbook for every chapter.

Learn more:

https://stringfestanalytics.com/pyxlda/

If you want to build these skills more systematically with practice files, exercises and a handbook, the full Python in Excel for Analysts course is opening soon at Stringfest Analytics.

Free-session attendees hear about it first.

Registration notes

To keep the session focused and useful:

• Please register using your real name.
• A work email is preferred where possible.
• Duplicate, placeholder or obviously incomplete registrations may be removed.
• Priority is given to people using Excel in a professional context.

About the end of the session

Toward the end, I'll briefly show you what else is covered in the book, mention the upcoming course and leave time for questions.

If your Excel models still come down to best case, base case and worst case, this session will show you what becomes possible when you can test 10,000 cases instead.