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Conda is "pip" for people who know _nothing_ about pip/operating-systems/IT. It is basically a way to have a working environment without understanding that maybe pandas and numpy and scikit are not Python built-in modules, and that maybe you'd need a working C/C++ (and even a Fortran compiler too) to install it.

It's great for that purpose, but - as far as I know - it has not much to do with general Python dependency management.



Conda does (in theory) handle python module dependencies well. In practice, like pip, sometimes you win and sometimes you lose.

Note that you can use 'pip' within a 'conda' environment as well, so you can sort of have it both ways.




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