Installing scikit-learn¶
Note
If you wish to contribute to the project, it’s recommended you install the latest development version.
Installing the latest release¶
Scikit-learn requires:
Python (>= 3.5)
NumPy (>= 1.11.0)
SciPy (>= 0.17.0)
joblib (>= 0.11)
Scikit-learn plotting capabilities (i.e., functions start with “plot_” and classes end with “Display”) require Matplotlib (>= 1.5.1). For running the examples Matplotlib >= 1.5.1 is required. A few examples require scikit-image >= 0.12.3, a few examples require pandas >= 0.18.0.
Warning
Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4. Scikit-learn now requires Python 3.5 or newer.
If you already have a working installation of numpy and scipy,
the easiest way to install scikit-learn is using pip
pip install -U scikit-learn
or conda:
conda install scikit-learn
If you have not installed NumPy or SciPy yet, you can also install these using conda or pip. When using pip, please ensure that binary wheels are used, and NumPy and SciPy are not recompiled from source, which can happen when using particular configurations of operating system and hardware (such as Linux on a Raspberry Pi). Building numpy and scipy from source can be complex (especially on Windows) and requires careful configuration to ensure that they link against an optimized implementation of linear algebra routines. Instead, use a third-party distribution as described below.
If you must install scikit-learn and its dependencies with pip, you can install
it as scikit-learn[alldeps]. The most common use case for this is in a
requirements.txt file used as part of an automated build process for a PaaS
application or a Docker image. This option is not intended for manual
installation from the command line.
Note
For installing on PyPy, PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+ are required.
For installation instructions for more distributions see other distributions. For compiling the development version from source, or building the package if no distribution is available for your architecture, see the Advanced installation instructions.
Third-party Distributions¶
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¶
Canopy and Anaconda both ship a recent version of scikit-learn, in addition to a large set of scientific python library for Windows, Mac OSX and Linux.
Anaconda offers scikit-learn as part of its free distribution.
Warning
To upgrade or uninstall scikit-learn installed with Anaconda
or conda you should not use the pip command. Instead:
To upgrade scikit-learn:
conda update scikit-learn
To uninstall scikit-learn:
conda remove scikit-learn
Upgrading with pip install -U scikit-learn or uninstalling
pip uninstall scikit-learn is likely fail to properly remove files
installed by the conda command.
pip upgrade and uninstall operations only work on packages installed
via pip install.