Yes, that is in the context of a giant and very successful team using this approach, believe or not. :)
Again, it's all a question of point of view, what we see as a package manager problem and causes us to keep reinventing packages managers, might actually be a problem with how we maintain our packages, my point of view being the latter. But I'm digressing.
When it comes to installing on "another machine", you don't know what Python they have, you don't know what libc they have, and so on, that is exactly what containers attempt to mitigate, so that seems exactly like the tool to use for this problem.
On the other hand, the users/sysadmins sometimes want to force the application to use different version of dependency, so the application may provide for that by somehow referencing the dependency from the ambient environment: usually it's done by either looking for a dependency in a well-known/hard-coded path, or getting that path from a well-known/hard-coded env var, or from a config file (which also you have to get from somewhere) or from some other injection/locator mechanism, thousands of those.
And all this stuff is bloody fractal: we have system-level packaging, then Python's own packaging on top of that, and then some particular Python application may decide to have its plugin-distribution system of sort (I've seen that), and that too goes on top of all of that, not to mention all the stuff happening in parallel (Qt sublibraries, GNOME modules, npm ecosystem)... well, you get the picture. It kinda reminds me of "Turing tarpit" and I doubt collapsing all this into one single layer of system-level packaging, and nothing on top, is really practical or even possible.
What about working on a project with a team?
What about deploying the code on another machine?