Using conda

The easiest and fastest way to get the package up and running is to install scikit-criteria using conda:

$ conda install -c conda-forge scikit-criteria

or, better yet, using mamba, which is a super fast replacement for conda:

$ conda install -c conda-forge mamba
$ mamba install -c conda-forge scikit-criteria


We encourage users to use conda or mamba and the conda-forge packages for convenience, especially when developing on Windows. It is recommended to create a new environment.

If the installation fails for any reason, please open an issue in the issue tracker.

Alternative installation methods

You can also install scikit-criteria from PyPI using pip:

$ pip install scikit-criteria

Finally, you can also install the latest development version of scikit-criteria directly from GitHub:

$ pip install git+

This is useful if there is some feature that you want to try, but we did not release it yet as a stable version. Although you might find some unpolished details, these development installations should work without problems. If you find any, please open an issue in the issue tracker.


It is recommended that you never ever use sudo with distutils, pip, setuptools and friends in Linux because you might seriously break your system [1] [2] [3] [4]. Use virtual environments instead.

If you don’t have Python

If you don’t already have a python installation with numpy and scipy, we recommend to install either via your package manager or via a python bundle. These come with numpy, scipy, matplotlib and many other helpful scientific and data processing libraries.

Canopy and Anaconda both ship a recent version of Python, in addition to a large set of scientific python library for Windows, Mac OSX and Linux.