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#Miniconda vs anaconda python how to#
#Miniconda vs anaconda python update#
#Miniconda vs anaconda python manual#
#Miniconda vs anaconda python code#

The PyPI wheels also contain the third-party binary dependencies like openblas inside the wheel. However, pip’s PyPI has Scipy, so what’s the accurate story here?

#Miniconda vs anaconda python code#

Many sources say that pip only handles Python code and conda can track many languages, and that this difference is important for packages using multiple languages e.g.

mambaforge is miniforge with mamba pre-installed.

miniforge and mambaforge are basic installers for a conda setup with conda-forge as the default source.

In contrast to defaults, it contains a magnitude more packages but also is run community-based and not curated by a single vendor. it provides a vast selection of packages like defaults.

conda-forge This is a community-maintained distribution, i.e.

mamba is an alternative implementation of conda that speeds up some parts (e.g.

If you want to create more environments and work a longer time with your conda setup, you should go with the miniconda distribution which only contains a minimal set of packages in the base environment. anaconda comes with a wide package selection is probably only useful if you want to quickly start.

anaconda and miniconda are both installers for conda-based environments.

conda is the package manager that you are using when you are talking about “anaconda”.

Anaconda is the company that mostly develops conda and also provides a vast set of curated packages in the defaults repository.

One of the things at first, is maybe to clear up some terms about (Ana)conda as I will use them later in my reply: Why can’t one package manager seamlessly accept packages installed by another package manager for the same language? Shouldn’t the package itself contain metadata both managers can use? When Anaconda lacks a package, a suggested fallback is to use pip within a conda environment, but it comes with tons of warnings of conda failing to manage version changes and conflicts, sometimes breaking apps like Spyder. Why is it hard for a package manager to manage a package not in its registry? For instance, I don’t know why I can’t just download a Python package and let conda track it like any other package, minus the simple commands for installs and updates from the registry. But what do people do when they need to mix languages, or build packages from source? There seems to be a lot more packages on PyPI than Anaconda’s registry, so that’s one point in favor of pip. Many sources say that pip only handles Python code and conda can track many languages, and that this difference is important for packages using multiple languages e.g.

#Miniconda vs anaconda python how to#

I feel like I’m awash with many sources on how to use several specific tools, but I lack general concepts on the why and how of installing and managing dependencies to make sense of it. I’m pretty sure package managers and virtual environments are how I should do this, but I’m really confused about what to use.

#Miniconda vs anaconda python update#

automated dependencies update mechanism, with conflict resolution of course.

make a copy of the versions specification that I can edit to update dependencies and see if the code breaks.

#Miniconda vs anaconda python manual#

automatically check for and fix conflicts among such dependencies and their own dependencies, give me manual control of version editing if necessary.explicit specification of dependencies and their versions.Easy conda install/ imports don’t seem good enough to keep a project stable anymore. Recently, I found some merit in mixing in some R via rpy2, and I’m also looking at Python packages that need pip for installation or to be built from source. So far in relatively simple Python projects, so I only used Anaconda for installing packages and never needed dependency management.