Conda works with a different, parallel ecosystem, whose main source of packages is managed by a single company (Anaconda Inc). That company validates, rebuilds, and possibly silently patches code, to provide their own packages.
pipenv brings simpler workflow to pip. pip leverages packages which are published by their authors onto pypi, which is managed by the Python Foundation.
> whose main source of packages is managed by a single company
thats not true today, conda-forge is a community led effort that have open source recipes of the packages built by anaconda inc and many more others contributed by the community. I run conda with conda-forge packages only and it works great.
My limited experience is that Conda is great if you are all-in on the parallel ecosystem, but it doesn't play well with others. Or at least, it didn't for me.
This may have been true at the beginning, but nowadays I use “pip install” extensively in conda-created environments. Are there particular packages that you have trouble with?
The problem with this is that pip will install dependencies of the package you're installing, not knowing (or caring) that those dependencies are already available in the conda repositories.
Later, conda may install a different version of the same dependency as a dependency of something else. Depending on how exactly they are installed (egg, zipped egg, whether the folder has the version number in it), you either get two versions of the same package installed, with which one gets imported being arbitrary, or you get two sets of metadata, with one of them not matching what is actually installed, such that pip may think version requirements are satisfied when they are not. It's messy as anything, and the breakage can be subtle. I distribute packages to users who use conda, and my packages have dependencies that are available in conda, so this has been messing with a lot of my users' installs. I'm now just making conda packages for these projects to solve the issue.
I made this package [1] to try and automate the process of making conda packages out of my existing setuptools packages, I'm quite happy with it but since it is designed to serve the needs of my projects, I can't guarantee it will suit everybody's needs.
Very different ecosystems. Its like recipe of pancakes on vegan and normal sites. It is doing the same but different target audience. For example, I rarely saw instruction for packages how to install it for conda except for cases when it is DS/ML oriented.
Conda is more generic - it allows packaging and distributing software written in languages other than Python. You can distribute java based software, C etc.
