subtitle:The fundamental package for scientific computing with Python

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</a><ahref=/contributeclass=navbar-item>Contribute</a><divclass="navbar-item has-dropdown"><aaria-label="Select language"class=navbar-link>English</a><divclass=navbar-dropdown><ahref=/pt/class=navbar-item>Português

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</a><ahref=/es/class=navbar-item>Español</a></div></div></div></div></div></nav><sectionclass=hero><divclass=hero-container><divclass=hero-content><divclass=hero-title-content><divclass=hero-title>NumPy

<imgclass=hero-logosrc=/images/logo.svgalt="NumPy logo."></div><divclass=flex-column><divclass=hero-subtitle>The fundamental package for scientific computing with Python</div><divclass=hero-cta><ahref=/news/#releases><buttonclass=cta-button>Latest release: NumPy 2.3. View all releases</button></a></div></div></div></div></div></section><divclass=news-container><divclass=news-title><ahref=/news>NumPy 2.3.0 released!</a></div><divclass=news-date><ahref=/news>2025-06-07</a></div></div><sectionclass=content-padding><divclass=content-container><divclass="sd-container-fluid sd-mb-4 false"><divclass="sd-row sd-row-cols-1 sd-row-cols-xs-1 sd-row-cols-sm-2 sd-row-cols-md-2 sd-row-cols-lg-3 sd-g-2 sd-g-xs-1 sd-g-sm-2 sd-g-md-2 sd-g-lg-3"><divclass="sd-col sd-d-flex-row"><divclass="sd-card sd-w-100 sd-shadow-sm"><divclass=sd-card-body><divclass="sd-card-title sd-font-weight-bold">Powerful N-dimensional arrays</div>Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.</div></div></div><divclass="sd-col sd-d-flex-row"><divclass="sd-card sd-w-100 sd-shadow-sm"><divclass=sd-card-body><divclass="sd-card-title sd-font-weight-bold">Numerical computing tools</div>NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.</div></div></div><divclass="sd-col sd-d-flex-row"><divclass="sd-card sd-w-100 sd-shadow-sm"><divclass=sd-card-body><divclass="sd-card-title sd-font-weight-bold">Open source</div>Distributed under a liberal<ahref=https://github.com/numpy/numpy/blob/main/LICENSE.txt>BSD license</a>, NumPy is developed and maintained<ahref=https://github.com/numpy/numpy>publicly on GitHub</a> by a vibrant, responsive, and diverse<ahref=/community/>community</a>.</div></div></div><divclass="sd-col sd-d-flex-row"><divclass="sd-card sd-w-100 sd-shadow-sm"><divclass=sd-card-body><divclass="sd-card-title sd-font-weight-bold">Interoperable</div>NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.</div></div></div><divclass="sd-col sd-d-flex-row"><divclass="sd-card sd-w-100 sd-shadow-sm"><divclass=sd-card-body><divclass="sd-card-title sd-font-weight-bold">Performant</div>The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.</div></div></div><divclass="sd-col sd-d-flex-row"><divclass="sd-card sd-w-100 sd-shadow-sm"><divclass=sd-card-body><divclass="sd-card-title sd-font-weight-bold">Easy to use</div>NumPy&rsquo;s high level syntax makes it accessible and productive for programmers from any background or experience level.</div></div></div></div></div></div></section><divclass=hero-right><divclass="flex-column shell-title-container"><divclass=shell-title>Try NumPy</div><divclass=shell-content-message><p>Use the interactive shell to try NumPy in the browser</p></div></div><divclass=numpy-shell-canvas><divclass=numpy-shell-container><divclass="shell-lesson shell-content"><divclass=highlight><preclass=chroma><code><spanstyle=display:flex><span><spanstyle=color:#e6db74>&#34;&#34;&#34;

<imgclass=hero-logosrc=/images/logo.svgalt="NumPy logo."></div><divclass=flex-column><divclass=hero-subtitle>The fundamental package for scientific computing with Python</div><divclass=hero-cta><ahref=/news/#releases><buttonclass=cta-button>Latest release: NumPy 2.4. View all releases</button></a></div></div></div></div></div></section><divclass=news-container><divclass=news-title><ahref=/news>NumPy 2.4.0 released!</a></div><divclass=news-date><ahref=/news>2025-12-20</a></div></div><sectionclass=content-padding><divclass=content-container><divclass="sd-container-fluid sd-mb-4 false"><divclass="sd-row sd-row-cols-1 sd-row-cols-xs-1 sd-row-cols-sm-2 sd-row-cols-md-2 sd-row-cols-lg-3 sd-g-2 sd-g-xs-1 sd-g-sm-2 sd-g-md-2 sd-g-lg-3"><divclass="sd-col sd-d-flex-row"><divclass="sd-card sd-w-100 sd-shadow-sm"><divclass=sd-card-body><divclass="sd-card-title sd-font-weight-bold">Powerful N-dimensional arrays</div>Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.</div></div></div><divclass="sd-col sd-d-flex-row"><divclass="sd-card sd-w-100 sd-shadow-sm"><divclass=sd-card-body><divclass="sd-card-title sd-font-weight-bold">Numerical computing tools</div>NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.</div></div></div><divclass="sd-col sd-d-flex-row"><divclass="sd-card sd-w-100 sd-shadow-sm"><divclass=sd-card-body><divclass="sd-card-title sd-font-weight-bold">Open source</div>Distributed under a liberal<ahref=https://github.com/numpy/numpy/blob/main/LICENSE.txt>BSD license</a>, NumPy is developed and maintained<ahref=https://github.com/numpy/numpy>publicly on GitHub</a> by a vibrant, responsive, and diverse<ahref=/community/>community</a>.</div></div></div><divclass="sd-col sd-d-flex-row"><divclass="sd-card sd-w-100 sd-shadow-sm"><divclass=sd-card-body><divclass="sd-card-title sd-font-weight-bold">Interoperable</div>NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.</div></div></div><divclass="sd-col sd-d-flex-row"><divclass="sd-card sd-w-100 sd-shadow-sm"><divclass=sd-card-body><divclass="sd-card-title sd-font-weight-bold">Performant</div>The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.</div></div></div><divclass="sd-col sd-d-flex-row"><divclass="sd-card sd-w-100 sd-shadow-sm"><divclass=sd-card-body><divclass="sd-card-title sd-font-weight-bold">Easy to use</div>NumPy&rsquo;s high level syntax makes it accessible and productive for programmers from any background or experience level.</div></div></div></div></div></div></section><divclass=hero-right><divclass="flex-column shell-title-container"><divclass=shell-title>Try NumPy</div><divclass=shell-content-message><p>Use the interactive shell to try NumPy in the browser</p></div></div><divclass=numpy-shell-canvas><divclass=numpy-shell-container><divclass="shell-lesson shell-content"><divclass=highlight><preclass=chroma><code><spanstyle=display:flex><span><spanstyle=color:#e6db74>&#34;&#34;&#34;

</span></span></span><spanstyle=display:flex><span><spanstyle=color:#e6db74>To try the examples in the browser:

</span></span></span><spanstyle=display:flex><span><spanstyle=color:#e6db74>1. Type code in the input cell and press

</span></span></span><spanstyle=display:flex><span><spanstyle=color:#e6db74> Shift + Enter to execute

<?xml version="1.0" encoding="utf-8" standalone="yes"?><rssversion="2.0"xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>NumPy</title><link>https://numpy.org/</link><description>Recent content on NumPy</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 07 Jun 2025 00:00:00 +0000</lastBuildDate><atom:linkhref="https://numpy.org/index.xml"rel="self"type="application/rss+xml"/><item><title>News</title><link>https://numpy.org/news/</link><pubDate>Sat, 07 Jun 2025 00:00:00 +0000</pubDate><guid>https://numpy.org/news/</guid><description>&lt;h3 id="numpy-230-released">NumPy 2.3.0 released&lt;a class="headerlink" href="#numpy-230-released" title="Link to this heading">#&lt;/a>&lt;/h3>

&lt;p>&lt;em>7 Jun, 2025&lt;/em>&amp;ndash; The NumPy 2.3.0 release improves free threaded Python support

and annotations together with the usual set of bug fixes. It is unusual in the

number of expired deprecations, code modernizations, and style cleanups. The

latter may not be visible to users, but is important for code maintenance over

the long term. Note that we have also upgraded from manylinux2014 to

manylinux_2_28. Highlights are:&lt;/p></description></item><item><title>2020 NUMPY COMMUNITY SURVEY</title><link>https://numpy.org/user-survey-2020/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://numpy.org/user-survey-2020/</guid><description>&lt;p>In 2020, the NumPy survey team in partnership with students and faculty from a

<?xml version="1.0" encoding="utf-8" standalone="yes"?><rssversion="2.0"xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>NumPy</title><link>https://numpy.org/</link><description>Recent content on NumPy</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 20 Dec 2025 00:00:00 +0000</lastBuildDate><atom:linkhref="https://numpy.org/index.xml"rel="self"type="application/rss+xml"/><item><title>News</title><link>https://numpy.org/news/</link><pubDate>Sat, 20 Dec 2025 00:00:00 +0000</pubDate><guid>https://numpy.org/news/</guid><description>&lt;h3 id="numpy-240-released">NumPy 2.4.0 released&lt;a class="headerlink" href="#numpy-240-released" title="Link to this heading">#&lt;/a>&lt;/h3>

&lt;p>&lt;em>20 Dec, 2025&lt;/em>&amp;ndash; The NumPy 2.4.0 release continues the work to improve free

threaded Python support, user dtypes implementation, and annotations. There are

many expired deprecations and bug fixes as well. Highlights are:&lt;/p>

&lt;ul>

&lt;li>Many annotation improvements. In particular, runtime signature introspection.&lt;/li>

&lt;li>New&lt;code>casting&lt;/code> kwarg&lt;code>'same_value'&lt;/code> for casting by value.&lt;/li>

&lt;li>New&lt;code>PyUFunc_AddLoopsFromSpec&lt;/code> function that can be used to add user sort

loops using the&lt;code>ArrayMethod&lt;/code> API.&lt;/li>

&lt;li>New&lt;code>__numpy_dtype__&lt;/code> protocol.&lt;/li>

&lt;/ul>

&lt;p>This release supports Python versions 3.11-3.14&lt;/p></description></item><item><title>2020 NUMPY COMMUNITY SURVEY</title><link>https://numpy.org/user-survey-2020/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://numpy.org/user-survey-2020/</guid><description>&lt;p>In 2020, the NumPy survey team in partnership with students and faculty from a

Master’s course in Survey Methodology jointly hosted by the University of

Michigan and the University of Maryland conducted the first official NumPy

community survey. Over 1,200 users from 75 countries participated to help us