As a Python developer, I just stay with pip and virtualenv, but I share the smh/wtf sentiment here. The data science folks enjoy conda but I don't see that being useful to me anywhere else. I feel like every other year someone will invent a new pip env.

pyvenv (see comment section, edited) is now deprecated and venv is recommended (shipped as part of Python 3.6 installation) is another confusion. Lest not forget the confusion of distuilts and setuptools is like argparse vs optparse in the past (which are both horrible). The experience of using pip and pypi (now Warehouse) is much better than that of Ruby and of NodeJS, but these "2-in-1" tools are just ridiculously "creative".

As always, pro-tip: consider using the following to ensure environment is loaded properly when you are deploying production

    /full_path/env/bin/python myapp.py --workers=3

over

    source /full_path/env/activate && python myapp.py --workers=3

The latter is fine when you are doing local development in your terminals.

If you go on #python IRC channel, every year a group of helpers will collectively recommend one of the above and then perhaps a different one the following year, so please do yourself a favor, just stick to pip and virtualenv.

If you want to not infect your current shell with the new environment, you can just run it in a subshell by using parentheses:

    $ ( . /full_path/env/bin/activate && python myapp.py --workers=3 )

This works for anything that you'd like a temporary shell for:

    $ pwd
    /home/me

    $ ( cd /tmp; pwd )
    /tmp

    $ pwd
    /home/me

> pyenv is now deprecated and venv is recommended

Isn't it `pyvenv` that is deprecated? `pyvenv` != `pyenv`

Yes, you are right. See, that's the other (#%#$^#$%#$ - excuse my Chinese) confusion. Such conflicting name pyvenv and pyenv, someone really could have picked a better name, whoever came later.

Corrected in my post.

Thanks for saying this! I just spent the last 10 minutes confused by that, trying to figure out what was going on here and what I actually have installed now:

⟩ pyenv --version

zsh: correct 'pyenv' to 'pyvenv' [nyae]?

Conda is especially useful if you want to use scientific packages on Windows.

Good point, thank you. I don't do much on Windows anymore. But a little search of VS Studio (I know there are some core envelopers working for MSFT) yields this: https://stackoverflow.com/questions/15185827/can-pip-be-used...

Probably exciting for VS users.

Yeah, I use pip + virtualenv on most machines (and have for years), but conda is the only one that actually seems to "just work" on Windows.

Apologies if I'm missing something obvious (relatively new to Python), but what's the advantage of loading it the 1st way? I'm currently using conda and do something similar to you're 2nd example `source activate conda-env && python myapp.py`

I'd like to hear what people think as well, so everyone please fire away. The 1st option is very explicit and doesn't modify your environment. The problem I have with the latter option is not just messing with my environment variable (bulk of it is setting PATH), but what will be changed in future releases. I don't have time to read the activate script (btw, there is one activate script for sh/bash/zsh, csh, fish, and bat, but if you are none of them, which likely a very corner case, you are on your own).

Using the 1st method, I am certain the shell isn't being modified and loaded with stuff I don't need, and the command and arguments are explicit (when I look at top, or when I do strace).

Generally, you can continue to do #2; I use it when I am doing development, like having multiple terminals up and just source into the environment, then run pytest instead of the full path to pytest. But I highly recommend #1 when you are running your code in production (webapp or not). I am not too familiar with conda so I will defer that to the experts).

python3.6 -m venv . && source bin/activate (this builds around the idea every project is a python package itself). pip install -e .

Well pyenv is to manage multiple versions of python, virtualenv is to create virtual environment, venv is virtualenv added to the stdlib, you forgot pip, virtualenvwrapper and mkvirtualenv are management tools for venvs and pipenv is also that but integrating pip.

There's pyenv, pip and virtualenv/venv (same thing) and then there's a bunch of tools to make them more implicit or ergonomic (if you like the way these tools work more than the base).

So really there's not a multitude of competing standards, there's 2/3 complementary standards and then people building their own tools atop that, not entirely unlike the multitudes of Jabber or Twitter clients we used to have.

Things were worse before virtualenv rose to prominence.

Please realize that the concept of virtual environments in Python predate things like npm, so there are lessons that were learned later on that no one knew about. Also realize that the things you list all layer on top of each other so you're listing lower-level libraries next to higher-level ones.

E.g. pipenv <- pew, pyenv <- virtualenv/venv, so that list could be likened to complaining that Python, C, assembly, and CPU microcode is "too many". Sure, you could argue they are usable independent of each other, but you aren't about to say that there are too many pieces of tech there and we should stop and sending raw electricity to the CPU.

I think that list could legitimately be cut down to pipenv and conda (maybe pipsi, but that's just for installing CLI tools and isn't for development). Everything else is lower-level than what most people will need for app development.

I use Buildout: http://docs.buildout.org/en/latest/

Buildout is much more than a Python package manager. It runs pluggable recipes, where building/installing a Python package is only one of the many available recipes. It was invented at a company where I once worked. It replaced huge piles of complicated Makefiles.

Buildout is obviously less popular than other solutions, but I think it manages complexity quite well and saves me a lot of time compared with other ways I could assemble Python software.

More than 5: I recently listed all programs for managing virtual environments I could find in a blog post [1] because I find it pretty confusing how many there are.

[1]: https://meribold.github.io/virtual-environments-9487

It's also super easy to build Python from source and manage the paths yourself, in case the magic tools fail to deliver the desired Python version.

There is only pip and virtualenv. They go together.

Virtualenv creates an application directory. Pip install the required packages into it.