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[Skip to main content](https://scikit-learn.org/stable/install.html#main-content) Back to top [ ](https://scikit-learn.org/stable/index.html) - [Install](https://scikit-learn.org/stable/install.html) - [User Guide](https://scikit-learn.org/stable/user_guide.html) - [API](https://scikit-learn.org/stable/api/index.html) - [Examples](https://scikit-learn.org/stable/auto_examples/index.html) - [Community](https://blog.scikit-learn.org/) - More - [Getting Started](https://scikit-learn.org/stable/getting_started.html) - [Release History](https://scikit-learn.org/stable/whats_new.html) - [Glossary](https://scikit-learn.org/stable/glossary.html) - [Development](https://scikit-learn.org/dev/developers/index.html) - [FAQ](https://scikit-learn.org/stable/faq.html) - [Support](https://scikit-learn.org/stable/support.html) - [Related Projects](https://scikit-learn.org/stable/related_projects.html) - [Roadmap](https://scikit-learn.org/stable/roadmap.html) - [Governance](https://scikit-learn.org/stable/governance.html) - [About us](https://scikit-learn.org/stable/about.html) - [GitHub](https://github.com/scikit-learn/scikit-learn) 1\.8.0 (stable) [1\.9.dev0 (dev)](https://scikit-learn.org/dev/install.html)[1\.8.0 (stable)](https://scikit-learn.org/stable/install.html)[1\.7.2](https://scikit-learn.org/1.7/install.html)[1\.6.1](https://scikit-learn.org/1.6/install.html)[1\.5.2](https://scikit-learn.org/1.5/install.html)[1\.4.2](https://scikit-learn.org/1.4/install.html)[1\.3.2](https://scikit-learn.org/1.3/install.html) - [Install](https://scikit-learn.org/stable/install.html) - [User Guide](https://scikit-learn.org/stable/user_guide.html) - [API](https://scikit-learn.org/stable/api/index.html) - [Examples](https://scikit-learn.org/stable/auto_examples/index.html) - [Community](https://blog.scikit-learn.org/) - [Getting Started](https://scikit-learn.org/stable/getting_started.html) - [Release History](https://scikit-learn.org/stable/whats_new.html) - [Glossary](https://scikit-learn.org/stable/glossary.html) - [Development](https://scikit-learn.org/dev/developers/index.html) - [FAQ](https://scikit-learn.org/stable/faq.html) - [Support](https://scikit-learn.org/stable/support.html) - [Related Projects](https://scikit-learn.org/stable/related_projects.html) - [Roadmap](https://scikit-learn.org/stable/roadmap.html) - [Governance](https://scikit-learn.org/stable/governance.html) - [About us](https://scikit-learn.org/stable/about.html) - [GitHub](https://github.com/scikit-learn/scikit-learn) 1\.8.0 (stable) [1\.9.dev0 (dev)](https://scikit-learn.org/dev/install.html)[1\.8.0 (stable)](https://scikit-learn.org/stable/install.html)[1\.7.2](https://scikit-learn.org/1.7/install.html)[1\.6.1](https://scikit-learn.org/1.6/install.html)[1\.5.2](https://scikit-learn.org/1.5/install.html)[1\.4.2](https://scikit-learn.org/1.4/install.html)[1\.3.2](https://scikit-learn.org/1.3/install.html) - Installing scikit-learn # Installing scikit-learn[\#](https://scikit-learn.org/stable/install.html#installing-scikit-learn "Link to this heading") There are different ways to install scikit-learn: - [Install the latest official release](https://scikit-learn.org/stable/install.html#install-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](https://scikit-learn.org/stable/install.html#install-by-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. - [Install a nightly build](https://scikit-learn.org/stable/install.html#install-nightly-builds). This is the quickest way to try a new feature that will be shipped in the next release (that is, a feature from a pull-request that was recently merged to the main branch); or to check whether a bug you encountered has been fixed since the last release. - [Building the package from source](https://scikit-learn.org/stable/developers/development_setup.html#setup-development-environment). This is mainly needed by users who wish to contribute to the project, as this allows to install an editable version of the project. ## Installing the latest release[\#](https://scikit-learn.org/stable/install.html#installing-the-latest-release "Link to this heading") Windows pip Install the 64-bit version of Python 3, for instance from the [official website](https://www.python.org/downloads/windows/). Now create a [virtual environment (venv)](https://docs.python.org/3/tutorial/venv.html) and install scikit-learn. Note that the virtual environment is optional but strongly recommended, in order to avoid potential conflicts with other packages. ``` python -m venv sklearn-env sklearn-env\Scripts\activate # activate pip install -U scikit-learn ``` In order to check your installation, you can use: ``` python -m pip show scikit-learn # show scikit-learn version and location python -m pip freeze # show all installed packages in the environment python -c "import sklearn; sklearn.show_versions()" ``` conda Install conda using the [conda-forge installers](https://conda-forge.org/download/) (no administrator permission required). Then run: ``` conda create -n sklearn-env -c conda-forge scikit-learn conda activate sklearn-env ``` In order to check your installation, you can use: ``` conda list scikit-learn # show scikit-learn version and location conda list # show all installed packages in the environment python -c "import sklearn; sklearn.show_versions()" ``` MacOS pip Install Python 3 using [homebrew](https://brew.sh/) (`brew install python`) or by manually installing the package from the [official website](https://www.python.org/downloads/macos/). Now create a [virtual environment (venv)](https://docs.python.org/3/tutorial/venv.html) and install scikit-learn. Note that the virtual environment is optional but strongly recommended, in order to avoid potential conflicts with other packages. ``` python -m venv sklearn-env source sklearn-env/bin/activate # activate pip install -U scikit-learn ``` In order to check your installation, you can use: ``` python -m pip show scikit-learn # show scikit-learn version and location python -m pip freeze # show all installed packages in the environment python -c "import sklearn; sklearn.show_versions()" ``` conda Install conda using the [conda-forge installers](https://conda-forge.org/download/) (no administrator permission required). Then run: ``` conda create -n sklearn-env -c conda-forge scikit-learn conda activate sklearn-env ``` In order to check your installation, you can use: ``` conda list scikit-learn # show scikit-learn version and location conda list # show all installed packages in the environment python -c "import sklearn; sklearn.show_versions()" ``` Linux pip Python 3 is usually installed by default on most Linux distributions. To check if you have it installed, try: ``` python3 --version pip3 --version ``` If you don’t have Python 3 installed, please install `python3` and `python3-pip` from your distribution’s package manager. Now create a [virtual environment (venv)](https://docs.python.org/3/tutorial/venv.html) and install scikit-learn. Note that the virtual environment is optional but strongly recommended, in order to avoid potential conflicts with other packages. ``` python3 -m venv sklearn-env source sklearn-env/bin/activate # activate pip3 install -U scikit-learn ``` In order to check your installation, you can use: ``` python3 -m pip show scikit-learn # show scikit-learn version and location python3 -m pip freeze # show all installed packages in the environment python3 -c "import sklearn; sklearn.show_versions()" ``` conda Install conda using the [conda-forge installers](https://conda-forge.org/download/) (no administrator permission required). Then run: ``` conda create -n sklearn-env -c conda-forge scikit-learn conda activate sklearn-env ``` In order to check your installation, you can use: ``` conda list scikit-learn # show scikit-learn version and location conda list # show all installed packages in the environment python -c "import sklearn; sklearn.show_versions()" ``` Using an isolated environment such as pip venv or conda 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 it is 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 starting with `plot_` and classes ending 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\.24.1 | build, install | | scipy | 1\.10.0 | build, install | | joblib | 1\.3.0 | install | | threadpoolctl | 3\.2.0 | install | | cython | 3\.1.2 | build | | meson-python | 0\.17.1 | build | | matplotlib | 3\.6.1 | benchmark, docs, examples, tests | | scikit-image | 0\.22.0 | docs, examples | | pandas | 1\.5.0 | benchmark, docs, examples, tests | | seaborn | 0\.13.0 | docs, examples | | memory\_profiler | 0\.57.0 | benchmark, docs | | pytest | 7\.1.2 | tests | | pytest-cov | 2\.9.0 | tests | | ruff | 0\.11.7 | tests | | mypy | 1\.15 | tests | | pyamg | 5\.0.0 | tests | | polars | 0\.20.30 | docs, tests | | pyarrow | 12\.0.0 | tests | | sphinx | 7\.3.7 | docs | | sphinx-copybutton | 0\.5.2 | docs | | sphinx-gallery | 0\.17.1 | docs | | numpydoc | 1\.2.0 | docs, tests | | Pillow | 10\.1.0 | docs | | pooch | 1\.8.0 | docs, examples, tests | | sphinx-prompt | 1\.4.0 | docs | | sphinxext-opengraph | 0\.9.1 | docs | | plotly | 5\.18.0 | docs, examples | | sphinxcontrib-sass | 0\.3.4 | docs | | sphinx-remove-toctrees | 1\.0.0.post1 | docs | | sphinx-design | 0\.6.0 | docs | | pydata-sphinx-theme | 0\.15.3 | docs | | towncrier | 24\.8.0 | docs | | conda-lock | 3\.0.1 | 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 required Python 3.6—3.8. Scikit-learn 0.24 required Python 3.6—3.9. Scikit-learn 1.0 supported Python 3.7—3.10. Scikit-learn 1.1, 1.2 and 1.3 supported Python 3.8—3.12. Scikit-learn 1.4 and 1.5 supported Python 3.9—3.12. Scikit-learn 1.6 supported Python 3.9—3.13. Scikit-learn 1.7 requires Python 3.10 or newer. ## Third party distributions of scikit-learn[\#](https://scikit-learn.org/stable/install.html#third-party-distributions-of-scikit-learn "Link to this heading") 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[\#](https://scikit-learn.org/stable/install.html#alpine-linux "Link to this heading") Alpine Linux’s package is provided through the [official repositories](https://pkgs.alpinelinux.org/packages?name=py3-scikit-learn) as `py3-scikit-learn` for Python. It can be installed by typing the following command: ``` sudo apk add py3-scikit-learn ``` ### Arch Linux[\#](https://scikit-learn.org/stable/install.html#arch-linux "Link to this heading") Arch Linux’s package is provided through the [official repositories](https://www.archlinux.org/packages/?q=scikit-learn) as `python-scikit-learn` for Python. It can be installed by typing the following command: ``` sudo pacman -S python-scikit-learn ``` ### Debian/Ubuntu[\#](https://scikit-learn.org/stable/install.html#debian-ubuntu "Link to this heading") The Debian/Ubuntu package is split in three different packages called `python3-sklearn` (python modules), `python3-sklearn-lib` (low-level implementations and bindings), `python-sklearn-doc` (documentation). Note that scikit-learn requires Python 3, hence the need to use the `python3-` suffixed package names. Packages can be installed using `apt-get`: ``` sudo apt-get install python3-sklearn python3-sklearn-lib python-sklearn-doc ``` ### Fedora[\#](https://scikit-learn.org/stable/install.html#fedora "Link to this heading") The Fedora package is called `python3-scikit-learn` for the python 3 version, the only one available in Fedora. It can be installed using `dnf`: ``` sudo dnf install python3-scikit-learn ``` ### NetBSD[\#](https://scikit-learn.org/stable/install.html#netbsd "Link to this heading") scikit-learn is available via [pkgsrc-wip](http://pkgsrc-wip.sourceforge.net/): <https://pkgsrc.se/math/py-scikit-learn> ### MacPorts for Mac OSX[\#](https://scikit-learn.org/stable/install.html#macports-for-mac-osx "Link to this heading") The MacPorts package is named `py<XY>-scikit-learn`, where `XY` denotes the Python version. It can be installed by typing the following command: ``` sudo port install py312-scikit-learn ``` ### Anaconda and Enthought Deployment Manager for all supported platforms[\#](https://scikit-learn.org/stable/install.html#anaconda-and-enthought-deployment-manager-for-all-supported-platforms "Link to this heading") [Anaconda](https://www.anaconda.com/download) and [Enthought Deployment Manager](https://assets.enthought.com/downloads/) 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[\#](https://scikit-learn.org/stable/install.html#intel-extension-for-scikit-learn "Link to this heading") 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](https://intel.github.io/scikit-learn-intelex/latest/what-is-patching.html) 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 [intel/scikit-learn-intelex](https://github.com/intel/scikit-learn-intelex). If you observe any issue with `scikit-learn-intelex`, please report the issue on their [issue tracker](https://github.com/intel/scikit-learn-intelex/issues). ### WinPython for Windows[\#](https://scikit-learn.org/stable/install.html#winpython-for-windows "Link to this heading") The [WinPython](https://winpython.github.io/) project distributes scikit-learn as an additional plugin. ## Troubleshooting[\#](https://scikit-learn.org/stable/install.html#troubleshooting "Link to this heading") If you encounter unexpected failures when installing scikit-learn, you may submit an issue to the [issue tracker](https://github.com/scikit-learn/scikit-learn/issues). Before that, please also make sure to check the following common issues. ### Error caused by file path length limit on Windows[\#](https://scikit-learn.org/stable/install.html#error-caused-by-file-path-length-limit-on-windows "Link to this heading") 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 ``` ## Installing nightly builds[\#](https://scikit-learn.org/stable/install.html#installing-nightly-builds "Link to this heading") The continuous integration servers of the scikit-learn project build, test and upload wheel packages for the most recent Python version on a nightly basis. You can install the nightly build of scikit-learn using the `scientific-python-nightly-wheels` index from the PyPI registry of `anaconda.org`: ``` pip install --pre --extra-index https://pypi.anaconda.org/scientific-python-nightly-wheels/simple scikit-learn ``` Note that first uninstalling scikit-learn might be required to be able to install nightly builds of scikit-learn. [previous Index](https://scikit-learn.org/stable/index.html "previous page") [next User Guide](https://scikit-learn.org/stable/user_guide.html "next page") On this page - [Installing the latest release](https://scikit-learn.org/stable/install.html#installing-the-latest-release) - [Third party distributions of scikit-learn](https://scikit-learn.org/stable/install.html#third-party-distributions-of-scikit-learn) - [Alpine Linux](https://scikit-learn.org/stable/install.html#alpine-linux) - [Arch Linux](https://scikit-learn.org/stable/install.html#arch-linux) - [Debian/Ubuntu](https://scikit-learn.org/stable/install.html#debian-ubuntu) - [Fedora](https://scikit-learn.org/stable/install.html#fedora) - [NetBSD](https://scikit-learn.org/stable/install.html#netbsd) - [MacPorts for Mac OSX](https://scikit-learn.org/stable/install.html#macports-for-mac-osx) - [Anaconda and Enthought Deployment Manager for all supported platforms](https://scikit-learn.org/stable/install.html#anaconda-and-enthought-deployment-manager-for-all-supported-platforms) - [Intel Extension for Scikit-learn](https://scikit-learn.org/stable/install.html#intel-extension-for-scikit-learn) - [WinPython for Windows](https://scikit-learn.org/stable/install.html#winpython-for-windows) - [Troubleshooting](https://scikit-learn.org/stable/install.html#troubleshooting) - [Error caused by file path length limit on Windows](https://scikit-learn.org/stable/install.html#error-caused-by-file-path-length-limit-on-windows) - [Installing nightly builds](https://scikit-learn.org/stable/install.html#installing-nightly-builds) ### This Page - [Show Source](https://scikit-learn.org/stable/_sources/install.rst.txt) © Copyright 2007 - 2026, scikit-learn developers (BSD License).

There are different ways to install scikit-learn: - [Install the latest official release](https://scikit-learn.org/stable/install.html#install-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](https://scikit-learn.org/stable/install.html#install-by-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. - [Install a nightly build](https://scikit-learn.org/stable/install.html#install-nightly-builds). This is the quickest way to try a new feature that will be shipped in the next release (that is, a feature from a pull-request that was recently merged to the main branch); or to check whether a bug you encountered has been fixed since the last release. - [Building the package from source](https://scikit-learn.org/stable/developers/development_setup.html#setup-development-environment). This is mainly needed by users who wish to contribute to the project, as this allows to install an editable version of the project. ## Installing the latest release[\#](https://scikit-learn.org/stable/install.html#installing-the-latest-release "Link to this heading") Windows pip Install the 64-bit version of Python 3, for instance from the [official website](https://www.python.org/downloads/windows/). Now create a [virtual environment (venv)](https://docs.python.org/3/tutorial/venv.html) and install scikit-learn. Note that the virtual environment is optional but strongly recommended, in order to avoid potential conflicts with other packages. ``` python -m venv sklearn-env sklearn-env\Scripts\activate # activate pip install -U scikit-learn ``` In order to check your installation, you can use: ``` python -m pip show scikit-learn # show scikit-learn version and location python -m pip freeze # show all installed packages in the environment python -c "import sklearn; sklearn.show_versions()" ``` conda Install conda using the [conda-forge installers](https://conda-forge.org/download/) (no administrator permission required). Then run: ``` conda create -n sklearn-env -c conda-forge scikit-learn conda activate sklearn-env ``` In order to check your installation, you can use: ``` conda list scikit-learn # show scikit-learn version and location conda list # show all installed packages in the environment python -c "import sklearn; sklearn.show_versions()" ``` MacOS pip Install Python 3 using [homebrew](https://brew.sh/) (`brew install python`) or by manually installing the package from the [official website](https://www.python.org/downloads/macos/). Now create a [virtual environment (venv)](https://docs.python.org/3/tutorial/venv.html) and install scikit-learn. Note that the virtual environment is optional but strongly recommended, in order to avoid potential conflicts with other packages. ``` python -m venv sklearn-env source sklearn-env/bin/activate # activate pip install -U scikit-learn ``` In order to check your installation, you can use: ``` python -m pip show scikit-learn # show scikit-learn version and location python -m pip freeze # show all installed packages in the environment python -c "import sklearn; sklearn.show_versions()" ``` conda Install conda using the [conda-forge installers](https://conda-forge.org/download/) (no administrator permission required). Then run: ``` conda create -n sklearn-env -c conda-forge scikit-learn conda activate sklearn-env ``` In order to check your installation, you can use: ``` conda list scikit-learn # show scikit-learn version and location conda list # show all installed packages in the environment python -c "import sklearn; sklearn.show_versions()" ``` Linux pip Python 3 is usually installed by default on most Linux distributions. To check if you have it installed, try: ``` python3 --version pip3 --version ``` If you don’t have Python 3 installed, please install `python3` and `python3-pip` from your distribution’s package manager. Now create a [virtual environment (venv)](https://docs.python.org/3/tutorial/venv.html) and install scikit-learn. Note that the virtual environment is optional but strongly recommended, in order to avoid potential conflicts with other packages. ``` python3 -m venv sklearn-env source sklearn-env/bin/activate # activate pip3 install -U scikit-learn ``` In order to check your installation, you can use: ``` python3 -m pip show scikit-learn # show scikit-learn version and location python3 -m pip freeze # show all installed packages in the environment python3 -c "import sklearn; sklearn.show_versions()" ``` conda Install conda using the [conda-forge installers](https://conda-forge.org/download/) (no administrator permission required). Then run: ``` conda create -n sklearn-env -c conda-forge scikit-learn conda activate sklearn-env ``` In order to check your installation, you can use: ``` conda list scikit-learn # show scikit-learn version and location conda list # show all installed packages in the environment python -c "import sklearn; sklearn.show_versions()" ``` Using an isolated environment such as pip venv or conda 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 it is 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 starting with `plot_` and classes ending 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\.24.1 | build, install | | scipy | 1\.10.0 | build, install | | joblib | 1\.3.0 | install | | threadpoolctl | 3\.2.0 | install | | cython | 3\.1.2 | build | | meson-python | 0\.17.1 | build | | matplotlib | 3\.6.1 | benchmark, docs, examples, tests | | scikit-image | 0\.22.0 | docs, examples | | pandas | 1\.5.0 | benchmark, docs, examples, tests | | seaborn | 0\.13.0 | docs, examples | | memory\_profiler | 0\.57.0 | benchmark, docs | | pytest | 7\.1.2 | tests | | pytest-cov | 2\.9.0 | tests | | ruff | 0\.11.7 | tests | | mypy | 1\.15 | tests | | pyamg | 5\.0.0 | tests | | polars | 0\.20.30 | docs, tests | | pyarrow | 12\.0.0 | tests | | sphinx | 7\.3.7 | docs | | sphinx-copybutton | 0\.5.2 | docs | | sphinx-gallery | 0\.17.1 | docs | | numpydoc | 1\.2.0 | docs, tests | | Pillow | 10\.1.0 | docs | | pooch | 1\.8.0 | docs, examples, tests | | sphinx-prompt | 1\.4.0 | docs | | sphinxext-opengraph | 0\.9.1 | docs | | plotly | 5\.18.0 | docs, examples | | sphinxcontrib-sass | 0\.3.4 | docs | | sphinx-remove-toctrees | 1\.0.0.post1 | docs | | sphinx-design | 0\.6.0 | docs | | pydata-sphinx-theme | 0\.15.3 | docs | | towncrier | 24\.8.0 | docs | | conda-lock | 3\.0.1 | 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 required Python 3.6—3.8. Scikit-learn 0.24 required Python 3.6—3.9. Scikit-learn 1.0 supported Python 3.7—3.10. Scikit-learn 1.1, 1.2 and 1.3 supported Python 3.8—3.12. Scikit-learn 1.4 and 1.5 supported Python 3.9—3.12. Scikit-learn 1.6 supported Python 3.9—3.13. Scikit-learn 1.7 requires Python 3.10 or newer. ## Third party distributions of scikit-learn[\#](https://scikit-learn.org/stable/install.html#third-party-distributions-of-scikit-learn "Link to this heading") 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[\#](https://scikit-learn.org/stable/install.html#alpine-linux "Link to this heading") Alpine Linux’s package is provided through the [official repositories](https://pkgs.alpinelinux.org/packages?name=py3-scikit-learn) as `py3-scikit-learn` for Python. It can be installed by typing the following command: ``` sudo apk add py3-scikit-learn ``` ### Arch Linux[\#](https://scikit-learn.org/stable/install.html#arch-linux "Link to this heading") Arch Linux’s package is provided through the [official repositories](https://www.archlinux.org/packages/?q=scikit-learn) as `python-scikit-learn` for Python. It can be installed by typing the following command: ``` sudo pacman -S python-scikit-learn ``` ### Debian/Ubuntu[\#](https://scikit-learn.org/stable/install.html#debian-ubuntu "Link to this heading") The Debian/Ubuntu package is split in three different packages called `python3-sklearn` (python modules), `python3-sklearn-lib` (low-level implementations and bindings), `python-sklearn-doc` (documentation). Note that scikit-learn requires Python 3, hence the need to use the `python3-` suffixed package names. Packages can be installed using `apt-get`: ``` sudo apt-get install python3-sklearn python3-sklearn-lib python-sklearn-doc ``` ### Fedora[\#](https://scikit-learn.org/stable/install.html#fedora "Link to this heading") The Fedora package is called `python3-scikit-learn` for the python 3 version, the only one available in Fedora. It can be installed using `dnf`: ``` sudo dnf install python3-scikit-learn ``` ### NetBSD[\#](https://scikit-learn.org/stable/install.html#netbsd "Link to this heading") scikit-learn is available via [pkgsrc-wip](http://pkgsrc-wip.sourceforge.net/): <https://pkgsrc.se/math/py-scikit-learn> ### MacPorts for Mac OSX[\#](https://scikit-learn.org/stable/install.html#macports-for-mac-osx "Link to this heading") The MacPorts package is named `py<XY>-scikit-learn`, where `XY` denotes the Python version. It can be installed by typing the following command: ``` sudo port install py312-scikit-learn ``` ### Anaconda and Enthought Deployment Manager for all supported platforms[\#](https://scikit-learn.org/stable/install.html#anaconda-and-enthought-deployment-manager-for-all-supported-platforms "Link to this heading") [Anaconda](https://www.anaconda.com/download) and [Enthought Deployment Manager](https://assets.enthought.com/downloads/) 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[\#](https://scikit-learn.org/stable/install.html#intel-extension-for-scikit-learn "Link to this heading") 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](https://intel.github.io/scikit-learn-intelex/latest/what-is-patching.html) 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 [intel/scikit-learn-intelex](https://github.com/intel/scikit-learn-intelex). If you observe any issue with `scikit-learn-intelex`, please report the issue on their [issue tracker](https://github.com/intel/scikit-learn-intelex/issues). ### WinPython for Windows[\#](https://scikit-learn.org/stable/install.html#winpython-for-windows "Link to this heading") The [WinPython](https://winpython.github.io/) project distributes scikit-learn as an additional plugin. ## Troubleshooting[\#](https://scikit-learn.org/stable/install.html#troubleshooting "Link to this heading") If you encounter unexpected failures when installing scikit-learn, you may submit an issue to the [issue tracker](https://github.com/scikit-learn/scikit-learn/issues). Before that, please also make sure to check the following common issues. ### Error caused by file path length limit on Windows[\#](https://scikit-learn.org/stable/install.html#error-caused-by-file-path-length-limit-on-windows "Link to this heading") 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 ``` ## Installing nightly builds[\#](https://scikit-learn.org/stable/install.html#installing-nightly-builds "Link to this heading") The continuous integration servers of the scikit-learn project build, test and upload wheel packages for the most recent Python version on a nightly basis. You can install the nightly build of scikit-learn using the `scientific-python-nightly-wheels` index from the PyPI registry of `anaconda.org`: ``` pip install --pre --extra-index https://pypi.anaconda.org/scientific-python-nightly-wheels/simple scikit-learn ``` Note that first uninstalling scikit-learn might be required to be able to install nightly builds of scikit-learn.