Why Python in Excel?

Many people do ask me why Python in Excel.

I hear it at conferences, in training rooms, on X, and during those quiet moments when an Excel power user bumps into the limits of VLOOKUP and starts dreaming in pandas.

The question isn't just about a feature. It's about a shift. A new way of working where two different worlds—Python and Excel—meet in the same workbook. And the real question hiding behind that first one is this:

What changes when Python meets Excel?

A Familiar Canvas, A New Brush

For years, Excel has been the tool of choice for data analysis, especially in corporate, finance, education, and public service spaces. It is where decisions start. Yet, Excel has always had blind spots. Advanced analytics, automation, and machine learning remained outside its comfort zone.

Then came Python.

With native Python integration, Excel now speaks a new language. Not a replacement, but a reinforcement.

You can clean, explore, and visualize data using libraries like pandas, Seaborn, and Matplotlib—without leaving the grid. No installations. No version headaches. Everything runs securely in Microsoft Cloud, powered by Anaconda. The Python cell behaves like a regular Excel formula, and Copilot can even help you write the code.

What’s Actually Happening?

When you write a Python formula in Excel (e.g., =PY()), you are not running Python on your local machine. Microsoft sends that code to the cloud. It executes within an Azure Container Instance. Results return and display in the cell. This separation allows scale, security, and consistency—but also brings trade-offs.

You cannot define custom Python functions using def. All execution must be stateless and within the constraints of the hosted runtime. The cloud model means your data is traveling, even if encrypted. Large datasets still slow things down. And collaboration in shared workbooks can be clunky when multiple users interact with the same Python code.

So it's powerful, but not magic.

Community Voices: What People Are Saying

On X, you’ll find voices like @itsafiz cheering the simplicity. “Python in Excel brings the power of Python analytics into Excel. Use it to process data with Python code,” he says.

Microsoft Excel’s own handle talks about democratizing analytics. No setup. Just code. Anaconda joins in with posts about workflow elevation and Copilot integration.

Yet, it`'s not all praise.

@felixzumstein points out limitations and promotes xlwings Lite—a local Python alternative that runs offline. Others like @soakdeinkue complain about the inability to define functions. Security researchers at BlackHatEvents raise concerns about potential cloud vulnerabilities. And financial professionals still hesitate: is this really safer than desktop-only tools?

The excitement is real. So is the caution.

Where It Shines: Real Use Cases

The integration is especially useful in financial planning and analysis. Think about reconciling bank transactions across two systems with slightly mismatched formats. Excel formulas might collapse under the logic. Python, with its flexible data handling, thrives here.

You can also create quick visualizations, run forecasts, cluster your sales data, or clean messy CSVs before sending your final report. The bonus? You don’t need to be a Python expert. Copilot can walk you through it.

Python in Excel allows for a hybrid approach—mixing Excel formulas, PivotTables, and Python logic in one place.

But when the data gets large, or the interaction becomes complex, Power BI still leads. Excel is not a database. Nor should it be one.

What About Alternatives?

Tools like xlwings Lite offer a different path. It runs Python inside the browser using WebAssembly (Pyodide), keeps everything local, and embeds the code directly in the workbook. This makes it great for offline work and privacy-sensitive environments.

But it also limits you. No multithreading. No extensive database access beyond SQLite. And no Copilot.

Choosing between Microsoft's integration and alternatives like xlwings is more about context than capability. Do you need cloud-powered collaboration, or do you value local control? Are you solving quick, spreadsheet-bound problems, or building pipelines that should live elsewhere?

Sometimes, the answer is Excel with Python. Sometimes, it's Python outside Excel.

Python in Excel is not just a feature. It is a statement. That spreadsheets are still evolving. That you don’t need to choose between ease of use and analytical depth. That data professionals can now do more without jumping tools.

But it is not perfect. Nor complete. The limitations around custom functions, collaboration quirks, and cloud security are real. So are the learning curves for those new to code.

Still, if you already live in Excel and have wondered how to go further—into automation, advanced visualization, or predictive analytics—then Python in Excel is worth exploring.

It's a bridge between familiarity and possibility.

And maybe that's the real answer to the question:

Why Python in Excel?

Because it invites Excel users to think like data scientists—without needing to leave the workbook behind.