The Open Source Framework for Machine Vision

# scikit-image: Image processing in Python

[![Image.sc forum](https://img.shields.io/badge/dynamic/json.svg?label=forum&url=https%3A%2F%2Fforum.image.sc%2Ftags%2Fscikit-image.json&query=%24.topic_list.tags.0.topic_count&colorB=brightgreen&suffix=%20topics&logo=data:image/png;base64,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)](https://forum.image.sc/tags/scikit-image)
[![Stackoverflow](https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg)](https://stackoverflow.com/questions/tagged/scikit-image)
[![project chat](https://img.shields.io/badge/zulip-join_chat-brightgreen.svg)](https://skimage.zulipchat.com)

- **Website (including documentation):** [https://scikit-image.org/](https://scikit-image.org)
- **User forum:** [https://forum.image.sc/tag/scikit-image](https://forum.image.sc/tag/scikit-image)
- **Developer forum:** [https://discuss.scientific-python.org/c/contributor/skimage](https://discuss.scientific-python.org/c/contributor/skimage)
- **Source:** [https://github.com/scikit-image/scikit-image](https://github.com/scikit-image/scikit-image)
- **Benchmarks:** [https://pandas.pydata.org/speed/scikit-image/](https://pandas.pydata.org/speed/scikit-image/)

## Installation from binaries

- **pip:** `pip install scikit-image`
- **conda:** `conda install -c conda-forge scikit-image`

Also see [installing `scikit-image`](INSTALL.rst).

## Installation from source

Install dependencies using:

```
pip install -r requirements.txt
```

Then, install scikit-image using:

```
$ pip install .
```

If you plan to develop the package, you may run it directly from source:

```
$ pip install -e .  # Do this once to add package to Python path
```

Every time you modify Cython files, also run:

```
$ python setup.py build_ext -i  # Build binary extensions
```

## License (Modified BSD)

Copyright (C) 2011, the scikit-image team
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:

1.  Redistributions of source code must retain the above copyright
    notice, this list of conditions and the following disclaimer.
2.  Redistributions in binary form must reproduce the above copyright
    notice, this list of conditions and the following disclaimer in
    the documentation and/or other materials provided with the
    distribution.
3.  Neither the name of skimage nor the names of its contributors may be
    used to endorse or promote products derived from this software without
    specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.

## Citation

If you find this project useful, please cite:

> Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias,
> François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle
> Gouillart, Tony Yu, and the scikit-image contributors.
> _scikit-image: Image processing in Python_. PeerJ 2:e453 (2014)
> https://doi.org/10.7717/peerj.453


scikit-image

<p align="center">
    <img width="248" height="250" src="https://raw.githubusercontent.com/python-pillow/pillow-logo/main/pillow-logo-248x250.png" alt="Pillow logo">
</p>

# Pillow

## Python Imaging Library (Fork)

Pillow is the friendly PIL fork by [Alex Clark and
Contributors](https://github.com/python-pillow/Pillow/graphs/contributors).
PIL is the Python Imaging Library by Fredrik Lundh and Contributors.
As of 2019, Pillow development is
[supported by Tidelift](https://tidelift.com/subscription/pkg/pypi-pillow?utm_source=pypi-pillow&utm_medium=readme&utm_campaign=enterprise).

<table>
    <tr>
        <th>docs</th>
        <td>
            <a href="https://pillow.readthedocs.io/?badge=latest"><img
                alt="Documentation Status"
                src="https://readthedocs.org/projects/pillow/badge/?version=latest"></a>
        </td>
    </tr>
    <tr>
        <th>tests</th>
        <td>
            <a href="https://github.com/python-pillow/Pillow/actions/workflows/lint.yml"><img
                alt="GitHub Actions build status (Lint)"
                src="https://github.com/python-pillow/Pillow/workflows/Lint/badge.svg"></a>
            <a href="https://github.com/python-pillow/Pillow/actions/workflows/test.yml"><img
                alt="GitHub Actions build status (Test Linux and macOS)"
                src="https://github.com/python-pillow/Pillow/workflows/Test/badge.svg"></a>
            <a href="https://github.com/python-pillow/Pillow/actions/workflows/test-windows.yml"><img
                alt="GitHub Actions build status (Test Windows)"
                src="https://github.com/python-pillow/Pillow/workflows/Test%20Windows/badge.svg"></a>
            <a href="https://github.com/python-pillow/Pillow/actions/workflows/test-mingw.yml"><img
                alt="GitHub Actions build status (Test MinGW)"
                src="https://github.com/python-pillow/Pillow/workflows/Test%20MinGW/badge.svg"></a>
            <a href="https://github.com/python-pillow/Pillow/actions/workflows/test-cygwin.yml"><img
                alt="GitHub Actions build status (Test Cygwin)"
                src="https://github.com/python-pillow/Pillow/workflows/Test%20Cygwin/badge.svg"></a>
            <a href="https://github.com/python-pillow/Pillow/actions/workflows/test-docker.yml"><img
                alt="GitHub Actions build status (Test Docker)"
                src="https://github.com/python-pillow/Pillow/workflows/Test%20Docker/badge.svg"></a>
            <a href="https://ci.appveyor.com/project/python-pillow/Pillow"><img
                alt="AppVeyor CI build status (Windows)"
                src="https://img.shields.io/appveyor/build/python-pillow/Pillow/main.svg?label=Windows%20build"></a>
            <a href="https://github.com/python-pillow/pillow-wheels/actions"><img
                alt="GitHub Actions wheels build status (Wheels)"
                src="https://github.com/python-pillow/pillow-wheels/workflows/Wheels/badge.svg"></a>
            <a href="https://app.travis-ci.com/github/python-pillow/pillow-wheels"><img
                alt="Travis CI wheels build status (aarch64)"
                src="https://img.shields.io/travis/com/python-pillow/pillow-wheels/main.svg?label=aarch64%20wheels"></a>
            <a href="https://app.codecov.io/gh/python-pillow/Pillow"><img
                alt="Code coverage"
                src="https://codecov.io/gh/python-pillow/Pillow/branch/main/graph/badge.svg"></a>
            <a href="https://bugs.chromium.org/p/oss-fuzz/issues/list?sort=-opened&can=1&q=proj:pillow"><img
                alt="Fuzzing Status"
                src="https://oss-fuzz-build-logs.storage.googleapis.com/badges/pillow.svg"></a>
        </td>
    </tr>
    <tr>
        <th>package</th>
        <td>
            <a href="https://zenodo.org/badge/latestdoi/17549/python-pillow/Pillow"><img
                alt="Zenodo"
                src="https://zenodo.org/badge/17549/python-pillow/Pillow.svg"></a>
            <a href="https://tidelift.com/subscription/pkg/pypi-pillow?utm_source=pypi-pillow&utm_medium=badge"><img
                alt="Tidelift"
                src="https://tidelift.com/badges/package/pypi/Pillow?style=flat"></a>
            <a href="https://pypi.org/project/Pillow/"><img
                alt="Newest PyPI version"
                src="https://img.shields.io/pypi/v/pillow.svg"></a>
            <a href="https://pypi.org/project/Pillow/"><img
                alt="Number of PyPI downloads"
                src="https://img.shields.io/pypi/dm/pillow.svg"></a>
            <a href="https://bestpractices.coreinfrastructure.org/projects/6331"><img
                alt="OpenSSF Best Practices"
                src="https://bestpractices.coreinfrastructure.org/projects/6331/badge"></a>
        </td>
    </tr>
    <tr>
        <th>social</th>
        <td>
            <a href="https://gitter.im/python-pillow/Pillow?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge"><img
                alt="Join the chat at https://gitter.im/python-pillow/Pillow"
                src="https://badges.gitter.im/python-pillow/Pillow.svg"></a>
            <a href="https://twitter.com/PythonPillow"><img
                alt="Follow on https://twitter.com/PythonPillow"
                src="https://img.shields.io/badge/tweet-on%20Twitter-00aced.svg"></a>
        </td>
    </tr>
</table>

## Overview

The Python Imaging Library adds image processing capabilities to your Python interpreter.

This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities.

The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool.

## More Information

- [Documentation](https://pillow.readthedocs.io/)
  - [Installation](https://pillow.readthedocs.io/en/latest/installation.html)
  - [Handbook](https://pillow.readthedocs.io/en/latest/handbook/index.html)
- [Contribute](https://github.com/python-pillow/Pillow/blob/main/.github/CONTRIBUTING.md)
  - [Issues](https://github.com/python-pillow/Pillow/issues)
  - [Pull requests](https://github.com/python-pillow/Pillow/pulls)
- [Release notes](https://pillow.readthedocs.io/en/stable/releasenotes/index.html)
- [Changelog](https://github.com/python-pillow/Pillow/blob/main/CHANGES.rst)
  - [Pre-fork](https://github.com/python-pillow/Pillow/blob/main/CHANGES.rst#pre-fork)

## Report a Vulnerability

To report a security vulnerability, please follow the procedure described in the [Tidelift security policy](https://tidelift.com/docs/security).


Pillow

Caffe: a fast open framework for deep learning.

TensorFlow Keras is an implementation of the Keras API that uses TensorFlow as
a backend.




keras

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

<div align="center">
  <img src="https://pandas.pydata.org/static/img/pandas.svg"><br>
</div>

-----------------

# pandas: powerful Python data analysis toolkit
[![PyPI Latest Release](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.org/project/pandas/)
[![Conda Latest Release](https://anaconda.org/conda-forge/pandas/badges/version.svg)](https://anaconda.org/anaconda/pandas/)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3509134.svg)](https://doi.org/10.5281/zenodo.3509134)
[![Package Status](https://img.shields.io/pypi/status/pandas.svg)](https://pypi.org/project/pandas/)
[![License](https://img.shields.io/pypi/l/pandas.svg)](https://github.com/pandas-dev/pandas/blob/main/LICENSE)
[![Coverage](https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=main)](https://codecov.io/gh/pandas-dev/pandas)
[![Downloads](https://static.pepy.tech/personalized-badge/pandas?period=month&units=international_system&left_color=black&right_color=orange&left_text=PyPI%20downloads%20per%20month)](https://pepy.tech/project/pandas)
[![Gitter](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/pydata/pandas)
[![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)

## What is it?

**pandas** is a Python package that provides fast, flexible, and expressive data
structures designed to make working with "relational" or "labeled" data both
easy and intuitive. It aims to be the fundamental high-level building block for
doing practical, **real world** data analysis in Python. Additionally, it has
the broader goal of becoming **the most powerful and flexible open source data
analysis / manipulation tool available in any language**. It is already well on
its way towards this goal.

## Main Features
Here are just a few of the things that pandas does well:

  - Easy handling of [**missing data**][missing-data] (represented as
    `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data
  - Size mutability: columns can be [**inserted and
    deleted**][insertion-deletion] from DataFrame and higher dimensional
    objects
  - Automatic and explicit [**data alignment**][alignment]: objects can
    be explicitly aligned to a set of labels, or the user can simply
    ignore the labels and let `Series`, `DataFrame`, etc. automatically
    align the data for you in computations
  - Powerful, flexible [**group by**][groupby] functionality to perform
    split-apply-combine operations on data sets, for both aggregating
    and transforming data
  - Make it [**easy to convert**][conversion] ragged,
    differently-indexed data in other Python and NumPy data structures
    into DataFrame objects
  - Intelligent label-based [**slicing**][slicing], [**fancy
    indexing**][fancy-indexing], and [**subsetting**][subsetting] of
    large data sets
  - Intuitive [**merging**][merging] and [**joining**][joining] data
    sets
  - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
    data sets
  - [**Hierarchical**][mi] labeling of axes (possible to have multiple
    labels per tick)
  - Robust IO tools for loading data from [**flat files**][flat-files]
    (CSV and delimited), [**Excel files**][excel], [**databases**][db],
    and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
  - [**Time series**][timeseries]-specific functionality: date range
    generation and frequency conversion, moving window statistics,
    date shifting and lagging


   [missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html
   [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion
   [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures
   [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine
   [conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe
   [slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges
   [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced
   [subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing
   [merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging
   [joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index
   [reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
   [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
   [mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex
   [flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files
   [excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files
   [db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries
   [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables
   [timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality

## Where to get it
The source code is currently hosted on GitHub at:
https://github.com/pandas-dev/pandas

Binary installers for the latest released version are available at the [Python
Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://docs.conda.io/en/latest/).

```sh
# conda
conda install pandas
```

```sh
# or PyPI
pip install pandas
```

## Dependencies
- [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org)
- [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html)
- [pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations](https://github.com/stub42/pytz)

See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) for minimum supported versions of required, recommended and optional dependencies.

## Installation from sources
To install pandas from source you need [Cython](https://cython.org/) in addition to the normal
dependencies above. Cython can be installed from PyPI:

```sh
pip install cython
```

In the `pandas` directory (same one where you found this file after
cloning the git repo), execute:

```sh
python setup.py install
```

or for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable):


```sh
python -m pip install -e . --no-build-isolation --no-use-pep517
```

If you have `make`, you can also use `make develop` to run the same command.

or alternatively

```sh
python setup.py develop
```

See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-from-source).

## License
[BSD 3](LICENSE)

## Documentation
The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable

## Background
Work on ``pandas`` started at [AQR](https://www.aqr.com/) (a quantitative hedge fund) in 2008 and
has been under active development since then.

## Getting Help

For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas).
Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata).

## Discussion and Development
Most development discussions take place on GitHub in this repo. Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Gitter channel](https://gitter.im/pydata/pandas) is available for quick development related questions.

## Contributing to pandas [![Open Source Helpers](https://www.codetriage.com/pandas-dev/pandas/badges/users.svg)](https://www.codetriage.com/pandas-dev/pandas)

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.

A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas.pydata.org/docs/dev/development/contributing.html)**.

If you are simply looking to start working with the pandas codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open) and [good first issue](https://github.com/pandas-dev/pandas/issues?labels=good+first+issue&sort=updated&state=open) where you could start out.

You can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to [subscribe to pandas on CodeTriage](https://www.codetriage.com/pandas-dev/pandas).

Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it!

Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Gitter](https://gitter.im/pydata/pandas).

As contributors and maintainers to this project, you are expected to abide by pandas' code of conduct. More information can be found at: [Contributor Code of Conduct](https://github.com/pandas-dev/.github/blob/master/CODE_OF_CONDUCT.md)


pandas

Tensors and Dynamic neural networks in Python with strong GPU acceleration

.. -*- mode: rst -*-

|Azure|_ |CirrusCI|_ |Codecov|_ |CircleCI|_ |Nightly wheels|_ |Black|_ |PythonVersion|_ |PyPi|_ |DOI|_ |Benchmark|_

.. |Azure| image:: https://dev.azure.com/scikit-learn/scikit-learn/_apis/build/status/scikit-learn.scikit-learn?branchName=main
.. _Azure: https://dev.azure.com/scikit-learn/scikit-learn/_build/latest?definitionId=1&branchName=main

.. |CircleCI| image:: https://circleci.com/gh/scikit-learn/scikit-learn/tree/main.svg?style=shield&circle-token=:circle-token
.. _CircleCI: https://circleci.com/gh/scikit-learn/scikit-learn

.. |CirrusCI| image:: https://img.shields.io/cirrus/github/scikit-learn/scikit-learn/main?label=Cirrus%20CI
.. _CirrusCI: https://cirrus-ci.com/github/scikit-learn/scikit-learn/main

.. |Codecov| image:: https://codecov.io/gh/scikit-learn/scikit-learn/branch/main/graph/badge.svg?token=Pk8G9gg3y9
.. _Codecov: https://codecov.io/gh/scikit-learn/scikit-learn

.. |Nightly wheels| image:: https://github.com/scikit-learn/scikit-learn/workflows/Wheel%20builder/badge.svg?event=schedule
.. _`Nightly wheels`: https://github.com/scikit-learn/scikit-learn/actions?query=workflow%3A%22Wheel+builder%22+event%3Aschedule

.. |PythonVersion| image:: https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-blue
.. _PythonVersion: https://pypi.org/project/scikit-learn/

.. |PyPi| image:: https://img.shields.io/pypi/v/scikit-learn
.. _PyPi: https://pypi.org/project/scikit-learn

.. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
.. _Black: https://github.com/psf/black

.. |DOI| image:: https://zenodo.org/badge/21369/scikit-learn/scikit-learn.svg
.. _DOI: https://zenodo.org/badge/latestdoi/21369/scikit-learn/scikit-learn

.. |Benchmark| image:: https://img.shields.io/badge/Benchmarked%20by-asv-blue
.. _`Benchmark`: https://scikit-learn.org/scikit-learn-benchmarks/

.. |PythonMinVersion| replace:: 3.8
.. |NumPyMinVersion| replace:: 1.17.3
.. |SciPyMinVersion| replace:: 1.3.2
.. |JoblibMinVersion| replace:: 1.1.1
.. |ThreadpoolctlMinVersion| replace:: 2.0.0
.. |MatplotlibMinVersion| replace:: 3.1.3
.. |Scikit-ImageMinVersion| replace:: 0.16.2
.. |PandasMinVersion| replace:: 1.0.5
.. |SeabornMinVersion| replace:: 0.9.0
.. |PytestMinVersion| replace:: 5.3.1
.. |PlotlyMinVersion| replace:: 5.10.0

.. image:: https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/doc/logos/scikit-learn-logo.png
  :target: https://scikit-learn.org/

**scikit-learn** is a Python module for machine learning built on top of
SciPy and is distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer
of Code project, and since then many volunteers have contributed. See
the `About us <https://scikit-learn.org/dev/about.html#authors>`__ page
for a list of core contributors.

It is currently maintained by a team of volunteers.

Website: https://scikit-learn.org

Installation
------------

Dependencies
~~~~~~~~~~~~

scikit-learn requires:

- Python (>= |PythonMinVersion|)
- NumPy (>= |NumPyMinVersion|)
- SciPy (>= |SciPyMinVersion|)
- joblib (>= |JoblibMinVersion|)
- threadpoolctl (>= |ThreadpoolctlMinVersion|)

=======

**Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.**
scikit-learn 1.0 and later require Python 3.7 or newer.
scikit-learn 1.1 and later require Python 3.8 or newer.

Scikit-learn plotting capabilities (i.e., functions start with ``plot_`` and
classes end with "Display") require Matplotlib (>= |MatplotlibMinVersion|).
For running the examples Matplotlib >= |MatplotlibMinVersion| is required.
A few examples require scikit-image >= |Scikit-ImageMinVersion|, a few examples
require pandas >= |PandasMinVersion|, some examples require seaborn >=
|SeabornMinVersion| and plotly >= |PlotlyMinVersion|.

User installation
~~~~~~~~~~~~~~~~~

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 -c conda-forge scikit-learn

The documentation includes more detailed `installation instructions <https://scikit-learn.org/stable/install.html>`_.


Changelog
---------

See the `changelog <https://scikit-learn.org/dev/whats_new.html>`__
for a history of notable changes to scikit-learn.

Development
-----------

We welcome new contributors of all experience levels. The scikit-learn
community goals are to be helpful, welcoming, and effective. The
`Development Guide <https://scikit-learn.org/stable/developers/index.html>`_
has detailed information about contributing code, documentation, tests, and
more. We've included some basic information in this README.

Important links
~~~~~~~~~~~~~~~

- Official source code repo: https://github.com/scikit-learn/scikit-learn
- Download releases: https://pypi.org/project/scikit-learn/
- Issue tracker: https://github.com/scikit-learn/scikit-learn/issues

Source code
~~~~~~~~~~~

You can check the latest sources with the command::

    git clone https://github.com/scikit-learn/scikit-learn.git

Contributing
~~~~~~~~~~~~

To learn more about making a contribution to scikit-learn, please see our
`Contributing guide
<https://scikit-learn.org/dev/developers/contributing.html>`_.

Testing
~~~~~~~

After installation, you can launch the test suite from outside the source
directory (you will need to have ``pytest`` >= |PyTestMinVersion| installed)::

    pytest sklearn

See the web page https://scikit-learn.org/dev/developers/contributing.html#testing-and-improving-test-coverage
for more information.

    Random number generation can be controlled during testing by setting
    the ``SKLEARN_SEED`` environment variable.

Submitting a Pull Request
~~~~~~~~~~~~~~~~~~~~~~~~~

Before opening a Pull Request, have a look at the
full Contributing page to make sure your code complies
with our guidelines: https://scikit-learn.org/stable/developers/index.html

Project History
---------------

The project was started in 2007 by David Cournapeau as a Google Summer
of Code project, and since then many volunteers have contributed. See
the `About us <https://scikit-learn.org/dev/about.html#authors>`__ page
for a list of core contributors.

The project is currently maintained by a team of volunteers.

**Note**: `scikit-learn` was previously referred to as `scikits.learn`.

Help and Support
----------------

Documentation
~~~~~~~~~~~~~

- HTML documentation (stable release): https://scikit-learn.org
- HTML documentation (development version): https://scikit-learn.org/dev/
- FAQ: https://scikit-learn.org/stable/faq.html

Communication
~~~~~~~~~~~~~

- Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
- Gitter: https://gitter.im/scikit-learn/scikit-learn
- Logos & Branding: https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
- Blog: https://blog.scikit-learn.org
- Calendar: https://blog.scikit-learn.org/calendar/
- Twitter: https://twitter.com/scikit_learn
- Twitter (commits): https://twitter.com/sklearn_commits
- Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn
- Github Discussions: https://github.com/scikit-learn/scikit-learn/discussions
- Website: https://scikit-learn.org
- LinkedIn: https://www.linkedin.com/company/scikit-learn
- YouTube: https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists
- Facebook: https://www.facebook.com/scikitlearnofficial/
- Instagram: https://www.instagram.com/scikitlearnofficial/
- TikTok: https://www.tiktok.com/@scikit.learn

Citation
~~~~~~~~

If you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn


Classroom SD Compliance Checker

Open Weaver – Develop Applications Faster with Open Source

kandi

Community and Support

Company

Follow