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<!doctype html><html lang=en data-colorscheme=light><head><meta name=description content="Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source."><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1,shrink-to-fit=no"><meta http-equiv=x-ua-compatible content="ie=edge"><title>NumPy - Array Computing</title>

Large scale data manipulation and transformation depends on efficient, high-performance array computing. The language of choice for data analytics, machine learning, and productive numerical computing is Python.

Numerical Python or NumPy is its de-facto standard Python programming language library that supports large, multi-dimensional arrays and matrices, and comes with a vast collection of high-level mathematical functions to operate on these arrays."></head><body><nav id=nav class=navbar role=navigation aria-label="main navigation"><div class=container><div class=navbar-brand><a class=navbar-item href=/><img class=navbar-logo src=/images/logo.svg alt="%!s(<nil>) logo"><div class=navbar-logo-text>NumPy</div></a><a role=button class=navbar-burger aria-label=menu aria-expanded=false data-target=navbar-menu><span aria-hidden=true></span>

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</a><a href=/es/arraycomputing/ class=navbar-item>Español</a></div></div></div></div></div></nav><section class=content-padding><div class=content-container><nav aria-label=Breadcrumb><ul id=breadcrumbs class=bd-breadcrumbs><li class="breadcrumb-item breadcrumb-home"><a href=/ class=nav-link aria-label=Home><i class="fas fa-home"></i></a></li><li class="breadcrumb-item active" aria-current=page>Array Computing</li></ul></nav><h1>Array Computing</h1><div><p><em>Array computing is the foundation of statistical, mathematical, scientific computing

in various contemporary data science and analytics applications such as data

visualization, digital signal processing, image processing, bioinformatics,

machine learning, AI, and several others.</em></p><p>Large scale data manipulation and transformation depends on efficient,

high-performance array computing. The language of choice for data analytics,

machine learning, and productive numerical computing is <strong>Python.</strong></p><p><strong>Num</strong>erical <strong>Py</strong>thon or NumPy is its de-facto standard Python programming

language library that supports large, multi-dimensional arrays and matrices,

and comes with a vast collection of high-level mathematical functions to

operate on these arrays.</p><p>Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008,

and it was not until a couple of years ago that several array computing

libraries showed up in succession, crowding the array computing landscape.

Many of these newer libraries mimic NumPy-like features and capabilities, and

pack newer algorithms and features geared towards machine learning and artificial intelligence applications.</p><p><img src=/images/content_images/array_c_landscape.png alt=arraycl title="Array Computing Landscape"></p><p><strong>Array computing</strong> is based on <strong>arrays</strong> data structures. <em>Arrays</em> are used

to organize vast amounts of data such that a related set of values can be easily

sorted, searched, mathematically manipulated, and transformed easily and quickly.</p><p>Array computing is <em>unique</em> as it involves operating on the data array <em>at

once</em>. What this means is that any array operation applies to an entire set of

values in one shot. This vectorized approach provides speed and simplicity by

enabling programmers to code and operate on aggregates of data, without having

to use loops of individual scalar operations.</p></div></div><div id=shortcuts-container><div id=shortcuts><div id=shortcuts-header><i class="fa-solid fa-list"></i> On this page</div></div></div></section><div id=backtotop><a href=# id=backtotop-color><i class="fa-solid fa-arrow-up"></i></a></div><footer id=footer><div class=container><div id=footer-columns><div id=footer-logo-column><img id=footer-logo src=/images/logo.svg alt="NumPy logo. "></div><div class=footer-column><div class=footer-item><a href=/install>Install</a></div><div class=footer-item><a href=https://numpy.org/doc/stable>Documentation</a></div><div class=footer-item><a href=/learn>Learn</a></div><div class=footer-item><a href=/citing-numpy>Citing NumPy</a></div><div class=footer-item><a href=https://numpy.org/neps/roadmap.html>Roadmap</a></div></div><div class=footer-column><div class=footer-item><a href=/about>About us</a></div><div class=footer-item><a href=/community>Community</a></div><div class=footer-item><a href=/user-surveys>User surveys</a></div><div class=footer-item><a href=/contribute>Contribute</a></div><div class=footer-item><a href=/code-of-conduct>Code of conduct</a></div></div><div class=footer-column><div class=footer-item><a href=/gethelp>Get help</a></div><div class=footer-item><a href=/terms>Terms of use</a></div><div class=footer-item><a href=/privacy>Privacy</a></div><div class=footer-item><a href=/press-kit>Press kit</a></div></div><div class=footer-actions>Sign up for the latest NumPy news, resources, and more<form action="https://numpy.us4.list-manage.com/subscribe/post?u=5ddd0d1d6e807900a8212481a&id=287fa4253c" method=post id=mc-embedded-subscribe-form name=mc-embedded-subscribe-form class="validate sign-up-container" target=_blank novalidate><div class=sign-up-image><svg class="icon mail-icon" viewBox="0 0 24 24" viewBox="0 0 24 24"><path d="M22 6c0-1.1-.9-2-2-2H4c-1.1.0-2 .9-2 2v12c0 1.1.9 2 2 2h16c1.1.0 2-.9 2-2V6zm-2 0-8 5-8-5h16zm0 12H4V8l8 5 8-5v10z"/></svg></div><input type=email name=EMAIL class="required email sign-up-input" id=mce-EMAIL aria-label="Input for email, press enter to submit" onkeypress='(event.which===13||event.keyCode===13||event.key==="Enter")&&sendThankYou()'><div class=submission-instructions>Press Enter</div><button class=signup-button onclick=sendThankYou() aria-label=Submit><svg class="icon sent-icon" viewBox="0 0 24 24" viewBox="0 0 24 24"><path d="M2.01 21 23 12 2.01 3 2 10l15 2-15 2z"/></svg></button><div id=mce-responses class=clear><div class=response id=mce-error-response style=display:none></div><div class=response id=mce-success-response style=display:none></div></div><div style=position:absolute;left:-5000px aria-hidden=true><input type=text name=b_5ddd0d1d6e807900a8212481a_287fa4253c tabindex=-1></div><div class=clear><input type=submit value=Subscribe name=subscribe id=mc-embedded-subscribe class=button style=display:none></div></form><div class=thank-you>Thank you! 🎉</div><div class=community-icons><a href=https://github.com/numpy/numpy aria-label=https://github.com/numpy/numpy><svg class="icon github-icon" viewBox="0 0 24 24" viewBox="0 0 24 24"><path d="M12 .297c-6.63.0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577.0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93.0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176.0.0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22.0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22.0 1.606-.015 2.896-.015 3.286.0.315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12"/></svg>

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