You don't want to be a developer. Fair enough — most people don't, and most jobs don't require it. But there's a version of Python that isn't about becoming a programmer. It's about having one more tool that makes certain parts of your job significantly faster.
What non-programmers actually use Python for
The practical use cases for Python in non-technical roles tend to cluster around the same things: cleaning up messy data, automating something repetitive, pulling information from multiple sources into one place, and building a quick analysis that would take hours in a spreadsheet.
A marketing analyst uses it to pull data from three different exports and merge them automatically instead of doing it by hand every Monday. An HR coordinator uses it to generate offer letter PDFs with custom fields instead of editing a template forty times. An operations manager uses it to flag anomalies in a daily report instead of scrolling through 800 rows looking for problems.
None of these people are programmers. They learned exactly enough Python to solve a specific, recurring problem. The time they saved paid for the learning within weeks.
The mental shift that helps
The people who get stuck learning Python are usually trying to learn "Python" as a subject rather than learning to solve a specific problem with Python. Start with the problem. What is the thing you wish you could do faster? Then learn the minimum Python needed to do that thing. You'll probably write ten lines of code that do something useful before you understand half of what you're writing. That's fine. Understanding comes through doing, not through reading documentation.
Where to start
The Pandas library is the most immediately useful entry point for most non-technical professionals. It's built for tabular data — the kind that lives in spreadsheets — and with a dozen basic commands you can do things that would take hours in Excel. Start there. You don't need to learn the whole language to be productive.