Ruby is mostly used in web dev, where most if not all of your dependencies tend to be pure Ruby.
Python is used heavily for DS/ML/AI, which is exactly the area where native code packages are necessary and prevalent. Worse yet is that those packages often involve GPU code, and things like CUDA bring their own complications.
If you're writing web apps in Python, dependencies haven't really been a problem for a long time now.
The reason python is a mess is because it became the default for academics and data scientists who have no real interest in code quality or maintainability and the rot just spread from there.
I've done a few Ruby/C and Python/C bindings, the APIs are worlds apart: I'd say Ruby's is like afternoon cocktails with a cultivated Swedish philosopher, Python's is the aftermath of a bar-room brawl where broken bottles were used.
> Ruby is mostly used in web dev, where most if not all of your dependencies tend to be pure Ruby.
There is literally no such thing as a rails app that’s pure ruby. Rails depends on nokogiri, which is a libxml2 wrapper, and all activerecord database adapters are C bindings. Ruby development involves dealing with frequent native extension compilation, just like python.
Those are all fine and easy dependencies to install in Python too though, lxml comes in as part of the Python standard lib, and database adaptors like psycopg all bundle the full source code needed.
Where it breaks down in Python is when the dependencies are enormous source code projects with their own dependencies in their own right, that you can't reasonably bundle in with your own project. Conda aimed to solve that by being cross-platform. In the old Python days you'd do 'pip install X' and it would fail because it expected the package to be available, on your system, and in the PATH/LD_LIBRARY_PATH. Then wheels (python packages that could bundle libraries rather than just source code) came along and people started bundling lowest-common-denominator versions that most people could use but at non-optimal performance, and that's roughly still where we are today.
Python is used heavily for DS/ML/AI, which is exactly the area where native code packages are necessary and prevalent. Worse yet is that those packages often involve GPU code, and things like CUDA bring their own complications.
If you're writing web apps in Python, dependencies haven't really been a problem for a long time now.