11–20 of 224 posts
Pyenv looks great! Sad I didn't know about this sooner, though I do have the luxury of using mostly one version of Python and have only confused it for the system Python once or twice.
there are way too many python dep/env managers/things pipenv pyenv mkvirtualenv virtualenv pipsi venv pew conda virtualenvwrapper i'm sure i'm forgetting like 5. this is like https://xkcd.com/927/ for the record i use pyenv and virtualenv (although playing with ML i'm using conda)
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
source /full_path/env/activate && python myapp.py --workers=3
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.
Every time I use Python I miss NPM and package.json.
I have no clue why a sane person would run npm.
there are way too many python dep/env managers/things pipenv pyenv mkvirtualenv virtualenv pipsi venv pew conda virtualenvwrapper i'm sure i'm forgetting like 5. this is like https://xkcd.com/927/ for the record i use pyenv and virtualenv (although playing with ML i'm using conda)
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…
Is there a command needed in pipenv like the one needed in virtualenv? eg. > source env/bin/activate How does one activate one environment over another? Why is pipsi a separate thing?
Pyenv looks great! Sad I didn't know about this sooner, though I do have the luxury of using mostly one version of Python and have only confused it for the system Python once or twice.
Pyenv is great. On our Macs we've had zero issues installing older, specific versions of Python, every time. Highly recommended. (Getting it working properly with zsh was a bit frustrating, but that's my own fault.)
there are way too many python dep/env managers/things pipenv pyenv mkvirtualenv virtualenv pipsi venv pew conda virtualenvwrapper i'm sure i'm forgetting like 5. this is like https://xkcd.com/927/ for the record i use pyenv and virtualenv (although playing with ML i'm using conda)
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…
Is there a command needed in pipenv like the one needed in virtualenv? eg. > source env/bin/activate How does one activate one environment over another? Why is pipsi a separate thing?
> pipenv run {command}
There's also:
> pipenv shell