![]() ![]() Since pip installs from source, it can be painful to install things with it if you are unable to compile the source code (this is especially true on Windows, but it can even be true on Linux if the packages have some difficult C or FORTRAN library dependencies). The first two bullet points of conda are really what make it advantageous over pip for many packages. Warning: While conda itself is open-source, the package repositories are hosted by Anaconda Inc and have restrictions around commercial usage.Open source (conda is BSD and pip is MIT).As of 2022, conda and pip are not fully aware of one another package management activities within a virtual environment, not are they interoperable for Python package management. ![]() You could use conda within an existing Python installation by pip installing it (though this is not recommended unless you have a good reason to use an existing installation). conda environment management functions cover the functionality provided by venv, virtualenv, pipenv, pyenv, and other Python-specific package managers. It is included in the Anaconda Python distribution provided by Continuum Analytics (now called Anaconda, Inc.).Ĭonda is an environment manager written in Python and is language-agnostic. conda is an environment and package manager. There is a tool called conda build that builds packages from source, but conda install itself installs things from already built Conda packages. The main focus of existing packages are for Python, and indeed Conda itself is written in Python, but you can also have Conda packages for C libraries, or R packages, or really anything. Blessed by the core Python community (i.e., Python 3.4+ includes code that automatically bootstraps pip).EDIT: pip now installs binary wheels, if they are available. See the more recent answer for more history. For reference, pip only gained widespread support for portable binary packages with wheels (pip 1.4 in 2013) and the manylinux1 specification (pip 8.1 in March 2016). Conda was specifically created to better support building and distributing binary packages, in particular data science libraries with C extensions. In these cases, it makes sense to try to use both conda and pip.ĭisclaimer: This answer describes the state of things as it was a decade ago, at that time pip did not support binary packages. Occasionally a package is needed which is not available as a conda package but is available on PyPI and can be installed with pip. Conda on the other hand can install Python packages as well as the Python interpreter directly. For example, before using pip, a Python interpreter must be installed via a system package manager or by downloading and running an installer. Pip installs Python packages whereas conda installs packages which may contain software written in any language. This highlights a key difference between conda and pip. Since writing this answer, Anaconda has published a new page on Understanding Conda and Pip, which echoes this as well: You can use the two tools side by side (by installing pip with conda install pip) but they do not interoperate either. Conda also creates a virtual environment, like virtualenv does.Īs such, Conda should be compared to Buildout perhaps, another tool that lets you handle both Python and non-Python installation tasks.īecause Conda introduces a new packaging format, you cannot use pip and Conda interchangeably pip cannot install the Conda package format. So Conda is a packaging tool and installer that aims to do more than what pip does handle library dependencies outside of the Python packages as well as the Python packages themselves. The main problem is that they are focused around Python, neglecting non-Python library dependencies, such as HDF5, MKL, LLVM, etc., which do not have a setup.py in their source code and also do not install files into Python’s site-packages directory. Having been involved in the python world for so long, we are all aware of pip, easy_install, and virtualenv, but these tools did not meet all of our specific requirements. ![]()
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