What is Python in Excel useful for if I already process spreadsheets with Python?

The handoff is one useful case. Say you build an analysis or plot that gets updated when new spreadsheet data arrives. Keeping the Python calculation in the workbook can make the result easier to use for someone who is comfortable in Excel but doesn't want to install and run a separate Python program.

That doesn't make the code magically understandable to the next person, though. They still need to know what inputs to update and how to interpret the output.

Financial models are a plausible fit, particularly where Excel is already the expected format. A project model could take monthly production, labor, fuel, maintenance and commodity prices as inputs, then use Python for a calculation that's awkward to express or maintain in formulas.

The benefit is keeping that calculation alongside the rest of the model. If the workbook is just an input file for a larger pipeline, external Python may still be simpler.

Check the execution environment before designing around it. Microsoft's integration runs Python in the cloud, even if you're working in desktop Excel. That raises a legitimate question about sending data and business logic for cloud processing, depending on your organization's policies.

Some people see that as a deal breaker. Others point out that organizations already using Microsoft cloud services have similar considerations for other data. Encryption in transit alone doesn't settle whether a particular workload is approved.

If your goal is automation rather than analysis inside a workbook, sticking with external Python is a perfectly reasonable answer. Reading and writing Excel files from a normal Python program keeps the spreadsheet as an input or output instead of making it the place where the processing lives.

There are also third party Excel integrations that take a different approach, including local execution. Check their capabilities and deployment requirements separately rather than assuming they work like Microsoft's feature.

Think of data analysis as another candidate: use spreadsheet data as input to Python calculations, then present the results in the workbook. Tables, data frames and plots are the kinds of things worth investigating.

For anything involving live API calls, scheduled jobs or placing outputs in specific locations, check the integration's current restrictions first. Those needs aren't automatically covered just because Python code can run in a cell.