Python, on the other hand, requires a python installer, then a pip or conda installation script. Not to mention python vs python3 decisions.
Conda installer comes with Python prepackaged. So it's really a single exe and one CMD to get a Tensorflow env setup. And, of course, you have access to numpy, pandas, jupyter, PIL ...
I always forget to set that up. Also I never learned python packaging, with npm it was trivial.
Seems pretty easy to me.
Plus, no one should be using Python 2 for any new project now, so that's not even a decision to bother with anymore.
Conda is like pip, but focused only on the sciences/data stuff. Pip has everything that Conda has and more as it's the standard packaging solution for Python.
Conda is more like pipenv in that it does both package management and environment/version management. And while it's true that not every python package is in conda, that vast majority are and conda and pip play nicely together if you want to use pip to install some packages.
Also conda can be used to install both surrounding tooling (like gcc and make, super useful on Windows) and, in many cases, build dependencies and necessary C libraries. Something pip doesn't handle. Doing for example "pip install gdal" will very likely leave you chasing down third party dependencies, while doing "conda install gdal" will just work.
Finally, conda has recently added R support. So you can now manage your R packages and environments with the same tools as python. Super handy if you have project that uses both R and python.