Ever since Microsoft introduced Python in Excel, I have treated it as a ‘someday’ project. I knew Python integration was the key to unlocking better automation and deeper insights, but the transition from traditional formulas to data frames comes with a steep learning curve.

That changed the moment I fed my documentation and messy notes into NotebookLM. I didn’t just learn how to write code; I learned how to think in Python within the familiar Excel grid.

The information overload problem

The documentation is vast

When I first opened the Python editor in Excel, I felt that familiar wave of information overload. I knew Python was capable of incredible things, but trying to learn it through traditional means was challenging.

I would spend twenty minutes Googling a specific Pandas error, only to realize the solution I found online didn’t actually apply to the unique way Python interacts with the Excel grid.

At one point, I had dozens of browser tabs open — Stack Overflow threads, official Microsoft documentation, and random YouTube tutorials — each offering a different piece of a puzzle I couldn’t quite assemble.

The problem wasn’t a lack of information; it was a lack of relevant information. I was dealing with generic Python syntax when I specifically needed to know how to handle the xl () function and data types within a spreadsheet environment.

I finally realized that if I didn’t find a better way to filter the noise, I was going to abandon Python in Excel before I ever wrote my first meaningful line of code.

Creating an ultimate notebook in NotebookLM

Make it as robust as possible

I realized that if I wanted to master this, I needed to stop searching the open web and start building my own private brain for the project. I decided to treat NotebookLM as a clean slate and fed it every high-quality resource I could find.

I started by downloading the official Microsoft documentation for Python in Excel and the core Pandas library docs. But I didn’t stop there. I found some of the best technical blogs that have cracked the Python integration in Excel.

I even pulled in transcripts from YouTube tutorials that explained the visual side of data plotting.

The bigger deal, though, was my own Obsidian vault. I exported my messy Markdown notes—the specific pain points I encountered, my half-finished ideas for automation, and my personal cheat sheets — and dropped them right into the notebook.

The transformation was instant. By combining all these sources — official specs, YouTube videos, and my own personal context — I created the ultimate closed-loop ecosystem.

I wasn’t just asking a generic AI for help anymore. I was carrying a specialized mentor who knew exactly what I was trying to build. Once my notebook was ready, I was all set to learn about Python in Excel.

Learning Python in Excel using NotebookLM

Advanced tricks discovered

Now, I was basically having a conversation with a Python expert in NotebookLM. Here are the kinds of questions I can ask directly in my NotebookLM notebook.

  • Why should users adopt Python when robust alternatives like VBA and Power Query already exist?
  • Which function enables Python formulas in cells?
  • State three core Python libraries available in Excel.
  • What’s the best way to visualize this specific thread without cluttering the cell?
  • I have a 12-level nested IFS statement in my 'Budget' tab. Based on the Pandas documentation I uploaded, how can I refactor this into a clean Python function using .map() or .apply()
  • Where are Python in Excel calculations executed and processed?

With each answer, NotebookLM also points out the source. At any point, I can click on the source and refer to the exact material from the sidebar. I also started generating Audio Overviews. It’s like having a custom podcast episode on Python in Excel.

There is even an option to disable several sources in a notebook. I can turn off several irrelevant sources so that NotebookLM can give me instant answers.

The entire notebook became a collaborative asset. I can share the entire notebook with my team. Instead of sending them a dry list of links or a complicated spreadsheet they didn’t understand, I gave them a living manual.

They could jump in and ask relevant questions. I wasn’t just learning faster; I was building a knowledge base that leveled up everyone around me.

From formulas to Python

Now, my only real frustration is thinking about the hours I spent manually cleaning rows and troubleshooting logic when a more elegant Python solution was just a prompt away. We often wait for the perfect time to learn a new skill, but as I have discovered, the perfect time was months ago.

So what are you waiting for? Create a notebook in NotebookLM, upload relevant materials, and fly through your complex Excel tasks. If you are still confused between VBA and Python for Excel automation, give this comparison a read.