Deploying to main from @ numpy/numpy.org@6840fca 🚀

Author: mattip

config.yaml

@@ -11,7 +11,7 @@ params:
     # Hero subtitle (optional)
     subtitle: The fundamental package for scientific computing with Python
     # Button text
-    buttontext: "Latest release: NumPy 2.3. View all releases"
+    buttontext: "Latest release: NumPy 2.4. View all releases"
     # Where the main hero button links to
     buttonlink: "/news/#releases"
     # Hero image (from static/images/___)

en/sitemap.xml

@@ -10,7 +10,7 @@
 </a><a href=/contribute class=navbar-item>Contribute</a><div class="navbar-item has-dropdown"><a aria-label="Select language" class=navbar-link>English</a><div class=navbar-dropdown><a href=/pt/ class=navbar-item>Português
 </a><a href=/ja/ class=navbar-item>日本語 (Japanese)
 </a><a href=/es/ class=navbar-item>Español</a></div></div></div></div></div></nav><section class=hero><div class=hero-container><div class=hero-content><div class=hero-title-content><div class=hero-title>NumPy
-<img class=hero-logo src=/images/logo.svg alt="NumPy logo. "></div><div class=flex-column><div class=hero-subtitle>The fundamental package for scientific computing with Python</div><div class=hero-cta><a href=/news/#releases><button class=cta-button>Latest release: NumPy 2.3. View all releases</button></a></div></div></div></div></div></section><div class=news-container><div class=news-title><a href=/news>NumPy 2.3.0 released!</a></div><div class=news-date><a href=/news>2025-06-07</a></div></div><section class=content-padding><div class=content-container><div class="sd-container-fluid sd-mb-4 false"><div class="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"><div class="sd-col sd-d-flex-row"><div class="sd-card sd-w-100 sd-shadow-sm"><div class=sd-card-body><div class="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><div class="sd-col sd-d-flex-row"><div class="sd-card sd-w-100 sd-shadow-sm"><div class=sd-card-body><div class="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><div class="sd-col sd-d-flex-row"><div class="sd-card sd-w-100 sd-shadow-sm"><div class=sd-card-body><div class="sd-card-title sd-font-weight-bold">Open source</div>Distributed under a liberal <a href=https://github.com/numpy/numpy/blob/main/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></div></div><div class="sd-col sd-d-flex-row"><div class="sd-card sd-w-100 sd-shadow-sm"><div class=sd-card-body><div class="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><div class="sd-col sd-d-flex-row"><div class="sd-card sd-w-100 sd-shadow-sm"><div class=sd-card-body><div class="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><div class="sd-col sd-d-flex-row"><div class="sd-card sd-w-100 sd-shadow-sm"><div class=sd-card-body><div class="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><div class=hero-right><div class="flex-column shell-title-container"><div class=shell-title>Try NumPy</div><div class=shell-content-message><p>Use the interactive shell to try NumPy in the browser</p></div></div><div class=numpy-shell-canvas><div class=numpy-shell-container><div class="shell-lesson shell-content"><div class=highlight><pre class=chroma><code><span style=display:flex><span><span style=color:#e6db74>&#34;&#34;&#34;
+<img class=hero-logo src=/images/logo.svg alt="NumPy logo. "></div><div class=flex-column><div class=hero-subtitle>The fundamental package for scientific computing with Python</div><div class=hero-cta><a href=/news/#releases><button class=cta-button>Latest release: NumPy 2.4. View all releases</button></a></div></div></div></div></div></section><div class=news-container><div class=news-title><a href=/news>NumPy 2.4.0 released!</a></div><div class=news-date><a href=/news>2025-12-20</a></div></div><section class=content-padding><div class=content-container><div class="sd-container-fluid sd-mb-4 false"><div class="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"><div class="sd-col sd-d-flex-row"><div class="sd-card sd-w-100 sd-shadow-sm"><div class=sd-card-body><div class="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><div class="sd-col sd-d-flex-row"><div class="sd-card sd-w-100 sd-shadow-sm"><div class=sd-card-body><div class="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><div class="sd-col sd-d-flex-row"><div class="sd-card sd-w-100 sd-shadow-sm"><div class=sd-card-body><div class="sd-card-title sd-font-weight-bold">Open source</div>Distributed under a liberal <a href=https://github.com/numpy/numpy/blob/main/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></div></div><div class="sd-col sd-d-flex-row"><div class="sd-card sd-w-100 sd-shadow-sm"><div class=sd-card-body><div class="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><div class="sd-col sd-d-flex-row"><div class="sd-card sd-w-100 sd-shadow-sm"><div class=sd-card-body><div class="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><div class="sd-col sd-d-flex-row"><div class="sd-card sd-w-100 sd-shadow-sm"><div class=sd-card-body><div class="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><div class=hero-right><div class="flex-column shell-title-container"><div class=shell-title>Try NumPy</div><div class=shell-content-message><p>Use the interactive shell to try NumPy in the browser</p></div></div><div class=numpy-shell-canvas><div class=numpy-shell-container><div class="shell-lesson shell-content"><div class=highlight><pre class=chroma><code><span style=display:flex><span><span style=color:#e6db74>&#34;&#34;&#34;
 </span></span></span><span style=display:flex><span><span style=color:#e6db74>To try the examples in the browser:
 </span></span></span><span style=display:flex><span><span style=color:#e6db74>1. Type code in the input cell and press
 </span></span></span><span style=display:flex><span><span style=color:#e6db74>   Shift + Enter to execute

index.html

@@ -1,10 +1,15 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="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:link href="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"?><rss version="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:link href="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