Pipenv VS conda

pipenv solves this by having both kinds of requirement files: Pipfile lists package names and known constraints on which versions can be used, while Pipfile.lock gives specific package versions with hashes. Theoretically the Pipfile (and its lockfile) format were supposed to be a standard that many different tools could use, but I haven't seen it get adopted much outside of pipenv itself, so I'm not sure if it's really going to catch on.

* Tons of longstanding bugs that fail to be resolved in any sort of timely manner like this one: https://github.com/pypa/pipenv/issues/2413

I am sick and tired of the python packaging space pushing this godawful tool for Python. Please just use pip-tools or poetry instead.

ℹ️ You may use a different virtual environment manager like Pipenv or poetry.

Hey lua would you mind looking at this question for me? https://github.com/pypa/pipenv/discussions/5411

Alternatively, you can look into Pipenv, which has a lot more tools to develop secure applications with.

https://github.com/pypa/pipenv/releases https://bodhi.fedoraproject.org/updates/?search=pipenv (pipenv-2021.5.29-7.fc35 for the current Fedora release)

Python's venv module is officially recommended for creating virtual environments since Python 3.5 comes packaged with your Python installation. While there still are additional older tools available, such as conda and virtualenv, if you are new to virtual environments, it is best to use venv now.

conda's dependency solver solves a harder problem than pip's. This quote alludes to it "Conda will never be as fast as pip, so long as we're doing real environment solves and pip satisfies itself only for the current operation." (from https://github.com/conda/conda/issues/7239). Thus mamba was created to improve performance and now conda is bringing in that performance boost.

The other challenge I ran into sometimes was that if I was running a lower memory/storage Codespace instance, when I tried to use Conda from the command line to modify environments, the process would be killed after a few seconds. This turns out to be related to some performance issues Conda has that make it consume a lot of memory when trying to work with the conda-forge installation channel. You can always then just increase the size of the Codespace your are working with (just go to your Codespaces list and use the triple dots to change the settings for a Codespace).

It's worth noting that [ana]conda isn't even fully compatible yet with 3.11 (you can use it to create 3.11 environments--and you really should rather than waiting on relying on the system python--but conda itself can only run on 3.10.

It took some time but as great Christmas present Miniconda was finally released with Python 3.10!

The September update broke it.

According to this this issue is high on their priority list (whatever that means).