If you wish to contribute to the project, it’s recommended you install the latest development version.
Installing the latest release¶
- Python (>= 2.6 or >= 3.3),
- NumPy (>= 1.6.1),
- SciPy (>= 0.9).
If you already have a working installation of numpy and scipy,
the easiest way to install scikit-learn is using
pip install -U scikit-learn
conda install scikit-learn
We don’t recommend installing scipy or numpy using pip on linux, as this will involve a lengthy build-process with many dependencies. Without careful configuration, building numpy yourself can lead to an installation that is much slower than it should be. If you are using Linux, consider using your package manager to install scikit-learn. It is usually the easiest way, but might not provide the newest version. If you haven’t already installed numpy and scipy and can’t install them via your operation system, it is recommended to use a third party distribution.
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, scikit-learn, matplotlib and many other helpful scientific and data processing libraries.
Available options are:
Canopy and Anaconda for all supported platforms¶
Anaconda offers scikit-learn as part of its free distribution.
To upgrade or uninstall scikit-learn installed with Anaconda
conda you should not use the pip command. Instead:
conda update scikit-learn
conda remove scikit-learn
pip install -U scikit-learn or uninstalling
pip uninstall scikit-learn is likely fail to properly remove files
installed by the
pip upgrade and uninstall operations only work on packages installed
Python(x,y) for Windows¶
For installation instructions for particular operating systems or for compiling the bleeding edge version, see the Advanced installation instructions.