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data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.
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 dark-light" 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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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 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 class=dark-light 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><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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