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I suspect the same. People adopt Python because its clean syntax and REPL make the language approachable and easy to get started and then the huge library ecosystem helps you glue together (in a good way) powerful systems. But how many Python users and libraries actually use the language’s extremely dynamic runtime behavior? Something they’re paying for with a big performance penalty and surprising bugs.

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Numpy uses magic methods (Python's version of operator overloading). Django uses metaclasses. Pytest dynamically rewrites assert statements using AST. I'd say it's pretty common, at least in libraries. I certainly depend on Python's dynamism when fully leveraging the REPL.

Indeed, these capabilities of the language are some of its strengths, even if we don't all trust ourselves to use them. The casual programmer doesn't know how or why numpy works, and we don't play with magic methods except out of curiosity. The people who do maintain numpy are using more formal methods to ensure its quality.

In the case of Python, the things that professional caliber programmers can use to extend its functionality, and the things that the rest of us can use to be productive, are the same language and are not hidden from one another. This is not the case in some languages of the past, e.g., you could use objects in Visual Basic but not build them without another language or eventually a special version.

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