Also there had been a growing trend for most popular packages to offer precompiled wheels on PyPI instead of just sdist releases.
This meant that people who had moved to Conda because they couldn't get Pip to install important packages into their environment took another look and found that actually they could get things installed using Pip now.
At the same time Pip also got a resolver allowing you to have install time confidence you're not installing conflicting package, and recently (Pip 23.1+) the resolver's backtracking got pretty good.
That said Conda mostly solved this (and once mamba is the default resolver engine things will be really fast), Pip is not ever going to be a package manager, and Poetry still isn't an environment manager, and most other Python package/installer alternatives to Conda won't do things like install your Jupyterlab's nodejs dependency.
After many years I now almost exclusively use Pip to install into an environment, but still nothing beats Conda for bootstraping the non-Python-package requirement's (such as Python itself) nor for getting things working when you are in a weird environment that you can't install OS dev libraries into.
Is Conda actually moving towards making mamba the default? Last I heard, they were distinctly uninterested in that, since mamba is implemented in C++, and they would rather rely on their own slow Python code, which they can more easily modify.
This meant that people who had moved to Conda because they couldn't get Pip to install important packages into their environment took another look and found that actually they could get things installed using Pip now.
At the same time Pip also got a resolver allowing you to have install time confidence you're not installing conflicting package, and recently (Pip 23.1+) the resolver's backtracking got pretty good.
That said Conda mostly solved this (and once mamba is the default resolver engine things will be really fast), Pip is not ever going to be a package manager, and Poetry still isn't an environment manager, and most other Python package/installer alternatives to Conda won't do things like install your Jupyterlab's nodejs dependency.
After many years I now almost exclusively use Pip to install into an environment, but still nothing beats Conda for bootstraping the non-Python-package requirement's (such as Python itself) nor for getting things working when you are in a weird environment that you can't install OS dev libraries into.