matplotlib

Python plotting package

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Project description

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Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

Check out our home page for more information.

Matplotlib produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, Python/IPython shells, web application servers, and various graphical user interface toolkits.

Install

See the install documentation, which is generated from /doc/install/index.rst

Contribute

You've discovered a bug or something else you want to change — excellent!

You've worked out a way to fix it — even better!

You want to tell us about it — best of all!

Start at the contributing guide!

Contact

Discourse is the discussion forum for general questions and discussions and our recommended starting point.

Our active mailing lists (which are mirrored on Discourse) are:

Users mailing list: matplotlib-users@python.org
Announcement mailing list: matplotlib-announce@python.org
Development mailing list: matplotlib-devel@python.org

Discourse Chat is for coordinating development and asking questions directly related to contributing to matplotlib.

Citing Matplotlib

If Matplotlib contributes to a project that leads to publication, please acknowledge this by citing Matplotlib.

A ready-made citation entry is available.

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Release history Release notifications | RSS feed

This version

3.11.1

Jul 18, 2026

3.11.0

Jun 12, 2026

3.11.0rc2 pre-release

May 13, 2026

3.11.0rc1 pre-release

Apr 24, 2026

3.10.9

Apr 24, 2026

3.10.8

Dec 10, 2025

3.10.7

Oct 9, 2025

3.10.6

Aug 30, 2025

3.10.5

Jul 31, 2025

3.10.3

May 8, 2025

3.10.1

Feb 27, 2025

3.10.0

Dec 14, 2024

3.10.0rc1 pre-release

Nov 1, 2024

3.9.4

Dec 13, 2024

3.9.3

Nov 30, 2024

3.9.2

Aug 13, 2024

3.9.1.post1

Aug 6, 2024

3.9.1 yanked

Jul 4, 2024

Reason this release was yanked:

The Windows wheels, under some conditions, caused segfaults in unrelated user code. Due to this we deleted the Windows wheels to prevent these segfaults, however this caused greater disruption as pip then began to try (and fail) to build 3.9.1 from the sdist on Windows which impacted far more users. Yanking the whole release is the only tool available to eliminate these failures without changes to on the user side. The sdist, OSX wheel, and manylinux wheels are all functional and there are no critical bugs in the release. Downstream packagers should not yank their builds of Matplotlib 3.9.1. See https://github.com/matplotlib/matplotlib/issues/28551 for details.

3.9.0

May 15, 2024

3.9.0rc2 pre-release

Apr 9, 2024

3.8.4

Apr 4, 2024

3.8.3

Feb 15, 2024

3.8.2

Nov 17, 2023

3.8.1

Nov 1, 2023

3.8.0

Sep 15, 2023

3.8.0rc1 pre-release

Aug 10, 2023

3.7.5

Feb 16, 2024

3.7.4

Nov 18, 2023

3.7.3

Sep 12, 2023

3.7.2

Jul 5, 2023

3.7.1

Mar 4, 2023

3.7.0

Feb 13, 2023

3.7.0rc1 pre-release

Jan 25, 2023

3.6.3

Jan 12, 2023

3.6.2

Nov 3, 2022

3.6.1

Oct 8, 2022

3.6.0

Sep 16, 2022

3.6.0rc2 pre-release

Aug 27, 2022

3.6.0rc1 pre-release

Aug 20, 2022

3.5.3

Aug 11, 2022

3.5.2

May 3, 2022

3.5.1

Dec 11, 2021

3.5.0

Nov 16, 2021

3.5.0rc1 pre-release

Oct 2, 2021

3.5.0b1 pre-release

Aug 24, 2021

3.4.3

Aug 13, 2021

3.4.2

May 8, 2021

3.4.1

Mar 31, 2021

3.4.0

Mar 26, 2021

3.4.0rc3 pre-release

Mar 11, 2021

3.4.0rc2 pre-release

Mar 11, 2021

3.4.0rc1 pre-release

Feb 19, 2021

3.3.4

Jan 28, 2021

3.3.3

Nov 12, 2020

3.3.2

Sep 15, 2020

3.3.1

Aug 14, 2020

3.3.0

Jul 16, 2020

3.3.0rc1 pre-release

Jun 18, 2020

3.2.2

Jun 17, 2020

3.2.1

Mar 18, 2020

3.2.0

Mar 4, 2020

3.2.0rc3 pre-release

Feb 3, 2020

3.2.0rc1 pre-release

Oct 25, 2019

3.1.3

Feb 3, 2020

3.1.2

Nov 21, 2019

3.1.1

Jul 3, 2019

3.1.0

May 18, 2019

3.1.0rc2 pre-release

May 6, 2019

3.1.0rc1 pre-release

Apr 19, 2019

3.0.3

Mar 1, 2019

3.0.2

Nov 11, 2018

3.0.1

Oct 25, 2018

3.0.0

Sep 18, 2018

3.0.0rc2 pre-release

Aug 28, 2018

2.2.5

Feb 3, 2020

2.2.4

Mar 1, 2019

2.2.3

Aug 11, 2018

2.2.2

Mar 17, 2018

2.2.0

Mar 6, 2018

2.2.0rc1 pre-release

Feb 14, 2018

2.1.2

Jan 21, 2018

2.1.1

Dec 12, 2017

2.1.0

Oct 7, 2017

2.1.0rc1 pre-release

Sep 2, 2017

2.0.2

May 10, 2017

2.0.1

May 2, 2017

2.0.0

Jan 17, 2017

2.0.0rc2 pre-release

Dec 19, 2016

2.0.0rc1 pre-release

Dec 6, 2016

2.0.0b4 pre-release

Sep 9, 2016

2.0.0b3 pre-release

Jul 17, 2016

2.0.0b2 pre-release

Jul 13, 2016

2.0.0b1 pre-release

May 31, 2016

1.5.3

Sep 9, 2016

1.5.2

Aug 18, 2016

1.5.1

Jan 11, 2016

1.5.0

Oct 30, 2015

1.4.3

Feb 17, 2015

1.4.2

Oct 23, 2014

1.4.1

Oct 18, 2014

1.4.1rc1 pre-release

Oct 14, 2014

1.4.0

Aug 26, 2014

1.3.1

Oct 21, 2013

1.3.0

Aug 1, 2013

1.2.1

Mar 26, 2013

1.2.0

Nov 9, 2012

1.1.1

Aug 19, 2012

1.1.0

Nov 3, 2011

1.0.1

Jan 6, 2011

1.0.0

Jul 6, 2010

0.99.3

Jun 1, 2010

0.99.1

Sep 22, 2009

0.99.0

Aug 30, 2009

0.98.5.3

May 21, 2009

0.98.5.1

Dec 16, 2008

0.98.3

Aug 3, 2008

0.98.1

Jun 23, 2008

0.98.0

May 31, 2008

0.91.4

Jun 23, 2008

0.91.3

May 31, 2008

0.91.2

Jan 6, 2008

0.91.1

Nov 30, 2007

0.91.0

Nov 28, 2007

0.90.1

Jun 2, 2007

0.90.0

Feb 7, 2007

0.87.7

Oct 26, 2006

0.87.6

Sep 27, 2006

0.87.5

Sep 5, 2006

0.87.4

Jul 12, 2006

0.87.3

Jun 7, 2006

0.87.2

Mar 16, 2006

0.87.1

Mar 7, 2006

0.86.2

Jan 24, 2006

0.86.1

Jan 12, 2006

0.86

Jan 9, 2006

0.85

Nov 16, 2005

0.83.2

Aug 2, 2005

0.71

Jan 24, 2005

0.70

Dec 30, 2004

0.65

Dec 14, 2004

0.64

Nov 8, 2004

0.63.0

Sep 28, 2004

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Size: 32.6 MB
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SHA256	`30c492d4ba9448595b6fd8708c6725963f8148e25c0d8842948da5b05f0ee8d3`
MD5	`0b0402ebe002e78f0967315e4b053249`
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Algorithm	Hash digest
SHA256	`216fbb93a74add02ddb4cb38ef5348f59ac00b3e84567eaf16598772d40e150a`
MD5	`9a6dfa3d65f8080676db041b0124d4d9`
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Algorithm	Hash digest
SHA256	`9a076f4fc5cdc43fdf510f5981418d25c2db4973418d9f22d8bb3dc8045ada78`
MD5	`2c2d3c95947687e0f36d1a7fc0208963`
BLAKE2b-256	`e50fa49c329d394f2e9ef38506982107e8b04ecf94dd41a9d8423ff82cc737c7`

Algorithm	Hash digest
SHA256	`83235693abde86e5e0129998f80ee39fc7f58e6d56a88fafb28a9278833e9d5f`
MD5	`087d47c0085b47e9b407b06e2f25058e`
BLAKE2b-256	`7a64cca444b4eb5e6c768c44fc5e1f0b5211f20ca2b282778051996e996a2bdf`

matplotlib 3.11.1

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Algorithm	Hash digest
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MD5	`6ccbe344a1517951c00b63b1589520f9`
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MD5	`a9f963254b934b80b0928ce8e2f2fa93`
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MD5	`a7396163557caf00072da1019f3f006a`
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MD5	`e552035ec01bc5b33ec3f657c17b13b1`
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MD5	`32624322944657785436f03fc2886688`
BLAKE2b-256	`e2db3f5ea5a5b64060ef5e1ff60a19170423e41ce21b8497a6fe15a36e0b43e3`

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MD5	`43eabf23deba03ecb1a1a0190f529139`
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MD5	`91989a79cbf1a9ff7f858aacc49b15d5`
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MD5	`1fd603aba5e13d69f7983e10fac5468f`
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MD5	`cd39e3eccee60e3e2dced112fb86e6a1`
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MD5	`c25cd256a19a956c3c0810d6562f5d32`
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