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Publishing Site numpy.org at 4676dfde24066315d4cd76f4d694cf555610632d on Wed Jun 23 19:03:09 UTC 2021
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config.yaml

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image: logos/numpy.svg
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# Customizable navbar. For a dropdown, add a "sublinks" list.
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news:
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title: NumPy v1.20.0
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content: Type annotation support - Performance improvements through multi-platform SIMD
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title: 2020 NumPy survey
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content: results are in
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url: /news
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shell:

index.html

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<a href=/community id=navbar-item class="navbar-item is-secondary">Community</a>
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<a href=/about id=navbar-item class="navbar-item is-secondary">About Us</a>
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<a href=/contribute id=navbar-item class="navbar-item is-secondary">Contribute</a></div></div></div></nav><div class=hero-container><div class=hero-content><div class=hero-title-content><div class=hero-title>NumPy
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<img class=hero-logo src=/images/logos/numpy.svg alt="Numpy logo. A three-dimensional blue cube with the letter 'N' highlighted in yellow."></div><div class=flex-column><div class=hero-subtitle>The fundamental package for scientific computing with Python</div><div class=hero-cta><a href=/install><button class=cta-button>Get started</button></a></div></div></div></div></div></section><div class=news-container><div class=news-title><a href=/news>NumPy v1.20.0</a></div><div class=news-content><a href=/news>Type annotation support - Performance improvements through multi-platform SIMD</a></div></div><section class=keyfeatures><div class=container><div class=keyfeatures-box-container><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Powerful N-dimensional arrays</div><div class=keyfeatures-box-text>Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.</div></p></div><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Numerical computing tools</div><div class=keyfeatures-box-text>NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.</div></p></div><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Interoperable</div><div class=keyfeatures-box-text>NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.</div></p></div><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Performant</div><div class=keyfeatures-box-text>The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.</div></p></div><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Easy to use</div><div class=keyfeatures-box-text>NumPy&rsquo;s high level syntax makes it accessible and productive for programmers from any background or experience level.</div></p></div><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Open source</div><div class=keyfeatures-box-text>Distributed under a liberal <a href=https://github.com/numpy/numpy/blob/master/LICENSE.txt>BSD license</a>, NumPy is developed and maintained <a href=https://github.com/numpy/numpy>publicly on GitHub</a> by a vibrant, responsive, and diverse <a href=/community>community</a>.</div></p></div></div></div></section><div class=hero-right><div class=numpy-shell-canvas><div class=numpy-shell-container><div class="flex-column shell-title-container"><div class="shell-intro shell-content"><div class=shell-title>Try NumPy</div><div class=shell-content-message>Enable the interactive shell</div></div><div class="shell-wait shell-content" style=display:none><div class=shell-title>While we wait...</div><div class=shell-content-message><p>Launching container on mybinder.org...</p><p>Don't forget to check out the <a href=https://numpy.org/doc/stable target=_blank>docs</a></p></div></div><div class="shell-lesson shell-content" style=display:none><div class=highlight><pre style=color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-python data-lang=python><span style=color:#f92672>&gt;&gt;&gt;</span> <span style=color:#75715e># The standard way to import NumPy:</span>
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<img class=hero-logo src=/images/logos/numpy.svg alt="Numpy logo. A three-dimensional blue cube with the letter 'N' highlighted in yellow."></div><div class=flex-column><div class=hero-subtitle>The fundamental package for scientific computing with Python</div><div class=hero-cta><a href=/install><button class=cta-button>Get started</button></a></div></div></div></div></div></section><div class=news-container><div class=news-title><a href=/news>2020 NumPy survey</a></div><div class=news-content><a href=/news>results are in</a></div></div><section class=keyfeatures><div class=container><div class=keyfeatures-box-container><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Powerful N-dimensional arrays</div><div class=keyfeatures-box-text>Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.</div></p></div><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Numerical computing tools</div><div class=keyfeatures-box-text>NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.</div></p></div><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Interoperable</div><div class=keyfeatures-box-text>NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.</div></p></div><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Performant</div><div class=keyfeatures-box-text>The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.</div></p></div><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Easy to use</div><div class=keyfeatures-box-text>NumPy&rsquo;s high level syntax makes it accessible and productive for programmers from any background or experience level.</div></p></div><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Open source</div><div class=keyfeatures-box-text>Distributed under a liberal <a href=https://github.com/numpy/numpy/blob/master/LICENSE.txt>BSD license</a>, NumPy is developed and maintained <a href=https://github.com/numpy/numpy>publicly on GitHub</a> by a vibrant, responsive, and diverse <a href=/community>community</a>.</div></p></div></div></div></section><div class=hero-right><div class=numpy-shell-canvas><div class=numpy-shell-container><div class="flex-column shell-title-container"><div class="shell-intro shell-content"><div class=shell-title>Try NumPy</div><div class=shell-content-message>Enable the interactive shell</div></div><div class="shell-wait shell-content" style=display:none><div class=shell-title>While we wait...</div><div class=shell-content-message><p>Launching container on mybinder.org...</p><p>Don't forget to check out the <a href=https://numpy.org/doc/stable target=_blank>docs</a></p></div></div><div class="shell-lesson shell-content" style=display:none><div class=highlight><pre style=color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-python data-lang=python><span style=color:#f92672>&gt;&gt;&gt;</span> <span style=color:#75715e># The standard way to import NumPy:</span>
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<span style=color:#f92672>&gt;&gt;&gt;</span> <span style=color:#f92672>import</span> numpy <span style=color:#f92672>as</span> np
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<span style=color:#f92672>&gt;&gt;&gt;</span> <span style=color:#75715e># Create a 2-D array, set every second element in</span>

index.xml

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StackOverflow A forum for asking usage questions, e.</description></item><item><title>History of NumPy</title><link>https://numpy.org/history/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://numpy.org/history/</guid><description>NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous.</description></item><item><title>Installing NumPy</title><link>https://numpy.org/install/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://numpy.org/install/</guid><description>The only prerequisite for installing NumPy is Python itself. If you don&amp;rsquo;t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science.
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NumPy can be installed with conda, with pip, with a package manager on macOS and Linux, or from source. For more detailed instructions, consult our Python and NumPy installation guide below.</description></item><item><title>Learn</title><link>https://numpy.org/learn/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://numpy.org/learn/</guid><description>For the official NumPy documentation visit numpy.org/doc/stable.
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NumPy Tutorials You can find a set of tutorials and educational materials by the NumPy community at NumPy Tutorials. The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the numpy-tutorials repository on GitHub.
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Below is a curated collection of external resources.</description></item><item><title>News</title><link>https://numpy.org/news/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://numpy.org/news/</guid><description>Numpy 1.20.0 release Jan 30, 2021 &amp;ndash; NumPy 1.20.0 is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are:
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Type annotations for large parts of NumPy, and a new numpy.typing submodule containing ArrayLike and DtypeLike aliases that users and downstream libraries can use when adding type annotations in their own code. Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions.</description></item><item><title>NumPy Code of Conduct</title><link>https://numpy.org/code-of-conduct/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://numpy.org/code-of-conduct/</guid><description>Introduction This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one.
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Below is a curated collection of external resources.</description></item><item><title>News</title><link>https://numpy.org/news/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://numpy.org/news/</guid><description>2020 NumPy survey results Jun 22, 2021 &amp;ndash; In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/.
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Numpy 1.20.0 release Jan 30, 2021 &amp;ndash; NumPy 1.20.0 is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are:</description></item><item><title>NumPy Code of Conduct</title><link>https://numpy.org/code-of-conduct/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://numpy.org/code-of-conduct/</guid><description>Introduction This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one.
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This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role.</description></item><item><title>NumPy Code of Conduct - How to follow up on a report</title><link>https://numpy.org/report-handling-manual/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://numpy.org/report-handling-manual/</guid><description>This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair.
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Enforcing the Code of Conduct impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind:
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Act in a personal manner rather than impersonal.</description></item><item><title>NumPy Diversity and Inclusion Statement</title><link>https://numpy.org/diversity_sep2020/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://numpy.org/diversity_sep2020/</guid><description>In light of the foregoing discussion on social media after publication of the NumPy paper in Nature and the concerns raised about the state of diversity and inclusion on the NumPy team, we would like to issue the following statement:

news/index.html

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<a href=/learn id=navbar-item class="navbar-item is-secondary">Learn</a>
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<a href=/community id=navbar-item class="navbar-item is-secondary">Community</a>
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<a href=/about id=navbar-item class="navbar-item is-secondary">About Us</a>
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<a href=/contribute id=navbar-item class="navbar-item is-secondary">Contribute</a></div></div></div></nav><section class="content-padding flex-row"><div class=shortcuts-container><div class=shortcuts-title>Shortcuts</div><div id=shortcuts></div><div class=sidebar-links><a href=/gethelp>Get Help</a></div></div><div class=content-container><div class=columns><div class="column is-centered-tablet-portrait"><h1 class="title section-title">News</h1><h5 class="subtitle is-5 is-muted"></h5><div class=divider></div></div></div><h3 id=numpy-1200-release>Numpy 1.20.0 release</h3><p><em>Jan 30, 2021</em> &ndash; <a href=https://numpy.org/doc/stable/release/1.20.0-notes.html>NumPy 1.20.0</a>
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<a href=/contribute id=navbar-item class="navbar-item is-secondary">Contribute</a></div></div></div></nav><section class="content-padding flex-row"><div class=shortcuts-container><div class=shortcuts-title>Shortcuts</div><div id=shortcuts></div><div class=sidebar-links><a href=/gethelp>Get Help</a></div></div><div class=content-container><div class=columns><div class="column is-centered-tablet-portrait"><h1 class="title section-title">News</h1><h5 class="subtitle is-5 is-muted"></h5><div class=divider></div></div></div><h3 id=2020-numpy-survey-results>2020 NumPy survey results</h3><p><em>Jun 22, 2021</em> &ndash; In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: <a href=https://numpy.org/user-survey-2020/>https://numpy.org/user-survey-2020/</a>.</p><h3 id=numpy-1200-release>Numpy 1.20.0 release</h3><p><em>Jan 30, 2021</em> &ndash; <a href=https://numpy.org/doc/stable/release/1.20.0-notes.html>NumPy 1.20.0</a>
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is now available. This is the largest NumPy release to date, thanks to 180+
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contributors. The two most exciting new features are:</p><ul><li>Type annotations for large parts of NumPy, and a new <code>numpy.typing</code> submodule
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containing <code>ArrayLike</code> and <code>DtypeLike</code> aliases that users and downstream

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