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I think you could probably use both, side-by-side (but not at the same time/for the same project). Anaconda might be nicer if the packages you need are C-based

I think you could probably use both, side-by-side (but not at the same time/for the same project). Anaconda might be nicer if the packages you need are C-based and would need compiling on your platform. Depends on your use-case. For experimentation/playing around, stick with what works for you. But if a project required me to install Anaconda, I wouldn’t take it seriously. And pipenv is pretty easy to use, too. So if you plan on distributing it or open-sourcing it, understanding how most other people manage dependencies ouside of conda is going to be useful.

stared 9y ago
Why not seriously? Many data science environments are pretty much based on Anaconda installations. (See e.g. these dockers: https://github.com/jupyter/docker-stacks/tree/master/scipy-notebook https://github.com/jupyter/docker-stacks/tree/master/scipy-n...) I mean, all packages I know DONT require Anaconda. But if you need the whole environment, sometimes Anaconda is the easiest tool too install all dependencies.
- guitarbill 9y ago
  > Anaconda is the easiest tool too install all dependencies. For hobbyist stuff that’s fine, and I applaud lowering the entry barrier. My issue would be if I needed Anaconda to deploy the project/dependencies into “prod” in some professional capacity, instead of standard Python build tools. Having said that, I’m not terribly familiar with Anaconda, and it seems to leverage virtualenv under the hood, possibly with pre-compiled packages (like wheels?).
  - stared 9y ago
    "Hobbyist"? It's widely used in research in data science & machine learning. If you want a setup which works on various systems, and uses Python numeric packages, usually it is the most failsafe way to use with various OS. (Unless you want to put everything in docker.) Unless by "professional" you mean "building", well - then you have a point.
    - Fomite 9y ago
      Yeah. Unless "Hobbyist" now also means my research university's 104 node, 2548 core HPC cluster...
  - pwang 9y ago
    Anaconda does not leverage virtualenv under the hood. Anaconda does not use wheels as part of that. However, you can use pip and virtualenvs within Anaconda, but you'll probably get some inconsistent results. The reason that Anaconda exists is to make it easier for people to have consistency between their dev environment, the build environment, and the prod environment. If you're seriously going to build all of the scientific Python stack from scratch, correctly and optimally, for your prod environment (and then also do the same for every dev environment you need to support), then you have way too much time and probably haven't actually tried doing it over any real period of time. The problem isn't that it's impossible. The problem is that there are dozens of different ways to almost succeed, and you don't run into the problems until way too late.
    - beagle3 9y ago
      > you can use pip and virtualenvs within Anaconda, but you'll probably get some inconsistent results. I've never tried virtualenvs inside anaconda (what would the use case be? anaconda already provides a virtual environment) pip is perfectly integrated within anaconda, in my experience; What inconsistencies are you talking about?

stared 9y ago

Why not seriously? Many data science environments are pretty much based on Anaconda installations. (See e.g. these dockers: https://github.com/jupyter/docker-stacks/tree/master/scipy-notebook https://github.com/jupyter/docker-stacks/tree/master/scipy-n...) I mean, all packages I know DONT require Anaconda. But if you need the whole environment, sometimes Anaconda is the easiest tool too install all dependencies.

guitarbill 9y ago
> Anaconda is the easiest tool too install all dependencies. For hobbyist stuff that’s fine, and I applaud lowering the entry barrier. My issue would be if I needed Anaconda to deploy the project/dependencies into “prod” in some professional capacity, instead of standard Python build tools. Having said that, I’m not terribly familiar with Anaconda, and it seems to leverage virtualenv under the hood, possibly with pre-compiled packages (like wheels?).
- stared 9y ago
  "Hobbyist"? It's widely used in research in data science & machine learning. If you want a setup which works on various systems, and uses Python numeric packages, usually it is the most failsafe way to use with various OS. (Unless you want to put everything in docker.) Unless by "professional" you mean "building", well - then you have a point.
  - Fomite 9y ago
    Yeah. Unless "Hobbyist" now also means my research university's 104 node, 2548 core HPC cluster...
- pwang 9y ago
  Anaconda does not leverage virtualenv under the hood. Anaconda does not use wheels as part of that. However, you can use pip and virtualenvs within Anaconda, but you'll probably get some inconsistent results. The reason that Anaconda exists is to make it easier for people to have consistency between their dev environment, the build environment, and the prod environment. If you're seriously going to build all of the scientific Python stack from scratch, correctly and optimally, for your prod environment (and then also do the same for every dev environment you need to support), then you have way too much time and probably haven't actually tried doing it over any real period of time. The problem isn't that it's impossible. The problem is that there are dozens of different ways to almost succeed, and you don't run into the problems until way too late.
  - beagle3 9y ago
    > you can use pip and virtualenvs within Anaconda, but you'll probably get some inconsistent results. I've never tried virtualenvs inside anaconda (what would the use case be? anaconda already provides a virtual environment) pip is perfectly integrated within anaconda, in my experience; What inconsistencies are you talking about?