A few days ago, I wrote about how to teach Python in Excel in a way that actually sticks:
The core idea was simple: you have to respect how Excel users already think.
When Excel users reach for Python, they are not looking to become Python developers overnight. They are looking to extend an existing mental model. They want something that fits naturally into their current stack, workflows, and instincts.
Unfortunately, a lot of Python in Excel training today misses that point. Much of it is essentially Python 101 with an Excel veneer. The training never really grapples with Python in Excel’s unique role inside the analytics stack.
That’s why I was excited to come across Python in Excel: Unlocking Powerful Data Analysis and Automation Solutions by Liam Bastick and Kathryn Newitt over at SumProduct.
There are many Python in Excel resources on the market, but this one stood out to me for how clearly it centers Excel users and their existing workflows.
Python in Excel as a natural progression, not a detour
What this book does exceptionally well is treat Python in Excel as a continuation of Excel’s analytics story rather than a bolt-on. The authors frame Python in Excel as a natural evolution of familiar tools from formulas and PivotTables to Power Query to Analyze Data and beyond.
Python doesn’t exist in a vacuum here. It is presented as another layer in Excel’s BI and analytics capabilities, not a replacement for what users already know.
Crucially, the book walks through how Python in Excel fits into real-world Excel workflows such as importing data from Power Query, working explicitly with tabular data (where Excel users actually live), and using Copilot effectively alongside Python.
That grounding makes a huge difference.
Constraints explained (and why they’re helpful)
Another strength is how clearly the book explains Python’s origin as an open-source ecosystem, and what that implies for packages, dependencies, and tooling.
Just as important, it explains why Python inside Excel comes with constraints, and why those constraints can actually be a feature. By limiting scope and environment complexity, Python in Excel becomes more approachable, safer, and easier to operationalize for everyday analysts. That framing helps users understand not just how Python in Excel works, but why it works the way it does.
Practical first, powerful later
What also works so well here is the sequencing. The book starts where Excel users actually live: structured tables, practical data manipulation, and quick analytical wins. That gives readers early confidence and a clear mental model for working with data, rather than throwing advanced concepts at them too soon.
By covering summaries, visualization, and common gotchas early, it helps readers avoid frustration and build good habits. Only after that foundation is in place do ideas like UDFs and control flow make sense. At that point, they feel like natural extensions of familiar workflows, not abstract hurdles. The book gets that progression right, which keeps it approachable without watering it down.
What I especially appreciated is how closely this aligns with how I try to teach Python in Excel myself: start with tabular thinking, real Excel workflows, and fast wins, then layer in power once the mental model is in place.
Final thoughts
This is a thoughtful, well-structured, and genuinely Excel-native approach to Python in Excel. It respects Excel users. It respects their workflows. And it shows Python in Excel not as a foreign language, but as a powerful extension of tools they already trust.
Bravo to Liam and Kathryn. I’ll absolutely be recommending this book to readers who want to understand Python in Excel the right way.