The biggest IMO is pip doesn't provide a way to pin indirect dependencies, so there is no good way to ensure that other people working on a project are also using the same dependency versions. You can do it with extra tooling of course - and that's what poetry and pipenv do.
When multiple versions of the same library are allowed to coexist in different dependency trees there is less incentive to solve these conflicts and versions proliferate (see npm).
I'd much rather solve the occasional conflict (usually solved either by settling on some older version of both parent dependencies - and later advancing as possible - or just trying to fix it and send a patch upstream).
Adding/removing top level dependencies over time does require the use of two files (the top level requirements and the frozen/locked requirements which lists everything). Or you can list the top level requirements in setup.py and let requirements.txt be the lockfile. It would be nice if pip managed this lockfile automatically, but I'm not really interested in adding any of these newer tools to my toolchain just to manage a lockfile.
There are many packaging and distribution frustrations in Python, I don't think pip's management of dependency lists is one of them
The problems are:
requirements.txt is way too flexible, so users want a lock file to freeze stuff in place.
You can use pip freeze to generate a sort-of-lockfile but it's more manual than tools like Cargo/npm/bundler which do it automatically. And more adhoc as to what you call it. Combined with needing to mange venvs and sourcing etc. people want a standard script that just figures out its context from the now standard "explicit requirements"/lockfile setup.
It doesn't handle versioning Python itself, which is expected of it for some reason, even though nobody cares that npm/Cargo/bundler don't version their languages.
There seems to be a lot of activity in this area for a seemingly solved problem. Having solved it, people appear to wish to expand the problem to something bigger (such as multiple interpreter versions, for example)
I believe the most-common case to be: 1 Choose python version and install on operating system., 2 create .venv/ directory. 3, install packages from package system (pip)., 4 freeze versions to local file. 5 use installed .venv/ and do work.
I've got some scripts I use for the above [1] -- must give it some TLC, and I don't understand why people furiously want more than this.
[1] https://github.com/gjvc/python-template-project