The availability of Python integration within Excel has made Python an accessible and valuable skill for many Excel users to consider adding to their toolkit. However, as an open-source programming language, Python operates differently from proprietary spreadsheet software like Excel. This post examines a significant distinction—the use of packages ...

After a pandemic-induced hiatus and move to fully virtual, the Global Excel Summit is BACK in hybrid format this February 2024, live from London, UK. I’m super excited to participate in person at this year’s event. Would you like to join me? Use promocode GEORGE at checkout to get 10% ...

In a previous post I explored some “quick wins” for wrangling and analyzing data in Python that would be otherwise difficult to do in Excel. That post largely avoided data visualization, so this post will be on a similar topic — visualizations that would be difficult to build in regular Excel ...

Look what touched down in my mailbox today… 🏈 _Football Analytics with Python & R_ by Eric Eager and Richard Erickson. I had the pleasure to be a tech reviewer for this book and recommend it to anyone interested in, as the subtitle suggests, learning data science through the lens of sports. ...

Excel’s popularity stems from its user-friendly data formatting and presentation capabilities. Take, for example, the nuisance of narrow columns. Resolving this issue in Excel is as simple as selecting the affected columns and double-clicking on any one of them: Python does present unique strengths to Excel, but visual formatting ...

In an earlier post I provided an overview of how to use the pandas and openpyxl packages together to automate the production of Excel workbooks. In that post, I likened pandas as the “Power Query of Python,” highlighting its role in cleaning and transforming tabular data. Readers might question the ...