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# Installing scikit-learn

There are different ways to install scikit-learn:

> - Install the latest official release. This is the best approach for most users. It will provide a stable version and pre-built packages are available for most platforms.

> - Install the version of scikit-learn provided by your operating system or Python distribution. This is a quick option for those who have operating systems or Python distributions that distribute scikit-learn. It might not provide the latest release version.

> - Building the package from source. This is best for users who want the latest-and-greatest features and aren’t afraid of running brand-new code. This is also needed for users who wish to contribute to the project.

## Installing the latest release

Operating System Windows macOS Linux
Packager pip conda
Install the 64bit version of Python 3, for instance from https://www.python.org.Install Python 3 using homebrew (`brew install python`) or by manually installing the package from https://www.python.org.Install python3 and python3-pip using the package manager of the Linux Distribution.Install conda using the Anaconda or miniconda installers or the miniforge installers (no administrator permission required for any of those).

Then run:

    pip3 install -U scikit-learn

    pip install -U scikit-learn

    pip install -U scikit-learn

    python3 -m venv sklearn-venv
    source sklearn-venv/bin/activate
    pip3 install -U scikit-learn

    python -m venv sklearn-venv
    sklearn-venv\Scripts\activate
    pip install -U scikit-learn

    python -m venv sklearn-venv
    source sklearn-venv/bin/activate
    pip install -U scikit-learn

    conda create -n sklearn-env -c conda-forge scikit-learn
    conda activate sklearn-env

In order to check your installation you can use

    python3 -m pip show scikit-learn  # to see which version and where scikit-learn is installed
    python3 -m pip freeze  # to see all packages installed in the active virtualenv
    python3 -c "import sklearn; sklearn.show_versions()"

    python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
    python -m pip freeze  # to see all packages installed in the active virtualenv
    python -c "import sklearn; sklearn.show_versions()"

    python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
    python -m pip freeze  # to see all packages installed in the active virtualenv
    python -c "import sklearn; sklearn.show_versions()"

    python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
    python -m pip freeze  # to see all packages installed in the active virtualenv
    python -c "import sklearn; sklearn.show_versions()"

    conda list scikit-learn  # to see which scikit-learn version is installed
    conda list  # to see all packages installed in the active conda environment
    python -c "import sklearn; sklearn.show_versions()"

Note that in order to avoid potential conflicts with other packages it is strongly recommended to use a virtual environment (venv) or a conda environment.

Using such an isolated environment makes it possible to install a specific version of scikit-learn with pip or conda and its dependencies independently of any previously installed Python packages. In particular under Linux is it discouraged to install pip packages alongside the packages managed by the package manager of the distribution (apt, dnf, pacman…).

Note that you should always remember to activate the environment of your choice prior to running any Python command whenever you start a new terminal session.

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).

Scikit-learn plotting capabilities (i.e., functions start with “plot_” and classes end with “Display”) require Matplotlib. The examples require Matplotlib and some examples require scikit-image, pandas, or seaborn. The minimum version of Scikit-learn dependencies are listed below along with its purpose.

Dependency | Minimum Version | Purpose
--- | --- | ---
numpy | 1.17.3 | build, install
scipy | 1.5.0 | build, install
joblib | 1.1.1 | install
threadpoolctl | 2.0.0 | install
cython | 0.29.33 | build
matplotlib | 3.1.3 | benchmark, docs, examples, tests
scikit-image | 0.16.2 | docs, examples, tests
pandas | 1.0.5 | benchmark, docs, examples, tests
seaborn | 0.9.0 | docs, examples
memory_profiler | 0.57.0 | benchmark, docs
pytest | 7.1.2 | tests
pytest-cov | 2.9.0 | tests
ruff | 0.0.272 | tests
black | 23.3.0 | tests
mypy | 1.3 | tests
pyamg | 4.0.0 | tests
sphinx | 6.0.0 | docs
sphinx-copybutton | 0.5.2 | docs
sphinx-gallery | 0.10.1 | docs
numpydoc | 1.2.0 | docs, tests
Pillow | 7.1.2 | docs
pooch | 1.6.0 | docs, examples, tests
sphinx-prompt | 1.3.0 | docs
sphinxext-opengraph | 0.4.2 | docs
plotly | 5.14.0 | docs, examples
conda-lock | 2.1.1 | maintenance

Warning

Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4. Scikit-learn 0.21 supported Python 3.5-3.7. Scikit-learn 0.22 supported Python 3.5-3.8. Scikit-learn 0.23 - 0.24 require Python 3.6 or newer. Scikit-learn 1.0 supported Python 3.7-3.10. Scikit-learn 1.1 and later requires Python 3.8 or newer.

## Third party distributions of scikit-learn

Some third-party distributions provide versions of scikit-learn integrated with their package-management systems.

These can make installation and upgrading much easier for users since the integration includes the ability to automatically install dependencies (numpy, scipy) that scikit-learn requires.

The following is an incomplete list of OS and python distributions that provide their own version of scikit-learn.

### Alpine Linux

Alpine Linux’s package is provided through the official repositories as `py3-scikit-learn` for Python. It can be installed by typing the following command:

    sudo apk add py3-scikit-learn

### Arch Linux

Arch Linux’s package is provided through the official repositories as `python-scikit-learn` for Python. It can be installed by typing the following command:

    sudo pacman -S python-scikit-learn

### Debian/Ubuntu

The Debian/Ubuntu package is split in three different packages called `python3-sklearn` (python modules), `python3-sklearn-lib` (low-level implementations and bindings), `python3-sklearn-doc` (documentation). Only the Python 3 version is available in the Debian Buster (the more recent Debian distribution). Packages can be installed using `apt-get`:

    sudo apt-get install python3-sklearn python3-sklearn-lib python3-sklearn-doc

### Fedora

The Fedora package is called `python3-scikit-learn` for the python 3 version, the only one available in Fedora30. It can be installed using `dnf`:

    sudo dnf install python3-scikit-learn

### NetBSD

scikit-learn is available via pkgsrc-wip:

> https://pkgsrc.se/math/py-scikit-learn

### MacPorts for Mac OSX

The MacPorts package is named `py<XY>-scikits-learn`, where `XY` denotes the Python version. It can be installed by typing the following command:

    sudo port install py39-scikit-learn

### Anaconda and Enthought Deployment Manager for all supported platforms

Anaconda and Enthought Deployment Manager both ship with 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.

### Intel Extension for Scikit-learn

Intel maintains an optimized x86_64 package, available in PyPI (via `pip`), and in the `main`, `conda-forge` and `intel` conda channels:

    conda install scikit-learn-intelex

This package has an Intel optimized version of many estimators. Whenever an alternative implementation doesn’t exist, scikit-learn implementation is used as a fallback. Those optimized solvers come from the oneDAL C++ library and are optimized for the x86_64 architecture, and are optimized for multi-core Intel CPUs.

Note that those solvers are not enabled by default, please refer to the scikit-learn-intelex documentation for more details on usage scenarios. Direct export example:

    from sklearnex.neighbors import NearestNeighbors

Compatibility with the standard scikit-learn solvers is checked by running the full scikit-learn test suite via automated continuous integration as reported on https://github.com/intel/scikit-learn-intelex. If you observe any issue with `scikit-learn-intelex`, please report the issue on their issue tracker.

### WinPython for Windows

The WinPython project distributes scikit-learn as an additional plugin.

## Troubleshooting

### Error caused by file path length limit on Windows

It can happen that pip fails to install packages when reaching the default path size limit of Windows if Python is installed in a nested location such as the `AppData` folder structure under the user home directory, for instance:

    C:\Users\username>C:\Users\username\AppData\Local\Microsoft\WindowsApps\python.exe -m pip install scikit-learn
    Collecting scikit-learn
    ...
    Installing collected packages: scikit-learn
    ERROR: Could not install packages due to an OSError: [Errno 2] No such file or directory: 'C:\\Users\\username\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\sklearn\\datasets\\tests\\data\\openml\\292\\api-v1-json-data-list-data_name-australian-limit-2-data_version-1-status-deactivated.json.gz'

In this case it is possible to lift that limit in the Windows registry by using the `regedit` tool:

1. Type “regedit” in the Windows start menu to launch `regedit`.

2. Go to the `Computer\HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem` key.

3. Edit the value of the `LongPathsEnabled` property of that key and set it to 1.

4. Reinstall scikit-learn (ignoring the previous broken installation):

    pip install --exists-action=i scikit-learn