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<imgclass=hero-logosrc=/images/logos/numpy.svgalt="Numpy logo. A three-dimensional blue cube with the letter 'N' highlighted in yellow."></div><divclass=flex-column><divclass=hero-subtitle>The fundamental package for scientific computing with Python</div><divclass=hero-cta><ahref=/install><buttonclass=cta-button>Get started</button></a></div></div></div></div></div></section><divclass=news-container><divclass=news-title><ahref=/news>2020 NumPy survey</a></div><divclass=news-content><ahref=/news>results are in</a></div></div><sectionclass=keyfeatures><divclass=container><divclass=keyfeatures-box-container><divclass="keyfeatures-box-content keyfeatures-underline"><p><divclass=keyfeatures-box-title>Powerful N-dimensional arrays</div><divclass=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><divclass="keyfeatures-box-content keyfeatures-underline"><p><divclass=keyfeatures-box-title>Numerical computing tools</div><divclass=keyfeatures-box-text>NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.</div></p></div><divclass="keyfeatures-box-content keyfeatures-underline"><p><divclass=keyfeatures-box-title>Interoperable</div><divclass=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><divclass="keyfeatures-box-content keyfeatures-underline"><p><divclass=keyfeatures-box-title>Performant</div><divclass=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><divclass="keyfeatures-box-content keyfeatures-underline"><p><divclass=keyfeatures-box-title>Easy to use</div><divclass=keyfeatures-box-text>NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.</div></p></div><divclass="keyfeatures-box-content keyfeatures-underline"><p><divclass=keyfeatures-box-title>Open source</div><divclass=keyfeatures-box-text>Distributed under a liberal <ahref=https://github.com/numpy/numpy/blob/master/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></p></div></div></div></section><divclass=hero-right><divclass=numpy-shell-canvas><divclass=numpy-shell-container><divclass="flex-column shell-title-container"><divclass="shell-intro shell-content"><divclass=shell-title>Try NumPy</div><divclass=shell-content-message>Enable the interactive shell</div></div><divclass="shell-wait shell-content" style=display:none><divclass=shell-title>While we wait...</div><divclass=shell-content-message><p>Launching container on mybinder.org...</p><p>Don’t forget to check out the <ahref=https://numpy.org/doc/stabletarget=_blank>docs</a>.</p></div></div><divclass="shell-lesson shell-content" style=display:none><divclass=highlight><prestyle=color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4><codeclass=language-pythondata-lang=python><spanstyle=color:#f92672>>>></span><spanstyle=color:#75715e># The standard way to import NumPy:</span>
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<imgclass=hero-logosrc=/images/logos/numpy.svgalt="Numpy logo. A three-dimensional blue cube with the letter 'N' highlighted in yellow."></div><divclass=flex-column><divclass=hero-subtitle>The fundamental package for scientific computing with Python</div><divclass=hero-cta><ahref=/install><buttonclass=cta-button>Get started</button></a></div></div></div></div></div></section><divclass=news-container><divclass=news-title><ahref=/news>2021 NumPy survey</a></div><divclass=news-content><ahref=/news>Your voice matters</a></div></div><sectionclass=keyfeatures><divclass=container><divclass=keyfeatures-box-container><divclass="keyfeatures-box-content keyfeatures-underline"><p><divclass=keyfeatures-box-title>Powerful N-dimensional arrays</div><divclass=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><divclass="keyfeatures-box-content keyfeatures-underline"><p><divclass=keyfeatures-box-title>Numerical computing tools</div><divclass=keyfeatures-box-text>NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.</div></p></div><divclass="keyfeatures-box-content keyfeatures-underline"><p><divclass=keyfeatures-box-title>Interoperable</div><divclass=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><divclass="keyfeatures-box-content keyfeatures-underline"><p><divclass=keyfeatures-box-title>Performant</div><divclass=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><divclass="keyfeatures-box-content keyfeatures-underline"><p><divclass=keyfeatures-box-title>Easy to use</div><divclass=keyfeatures-box-text>NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.</div></p></div><divclass="keyfeatures-box-content keyfeatures-underline"><p><divclass=keyfeatures-box-title>Open source</div><divclass=keyfeatures-box-text>Distributed under a liberal <ahref=https://github.com/numpy/numpy/blob/master/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></p></div></div></div></section><divclass=hero-right><divclass=numpy-shell-canvas><divclass=numpy-shell-container><divclass="flex-column shell-title-container"><divclass="shell-intro shell-content"><divclass=shell-title>Try NumPy</div><divclass=shell-content-message>Enable the interactive shell</div></div><divclass="shell-wait shell-content" style=display:none><divclass=shell-title>While we wait...</div><divclass=shell-content-message><p>Launching container on mybinder.org...</p><p>Don’t forget to check out the <ahref=https://numpy.org/doc/stabletarget=_blank>docs</a>.</p></div></div><divclass="shell-lesson shell-content" style=display:none><divclass=highlight><prestyle=color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4><codeclass=language-pythondata-lang=python><spanstyle=color:#f92672>>>></span><spanstyle=color:#75715e># The standard way to import NumPy:</span>
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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&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 1.21.0 release Jun 23, 2021 &ndash; NumPy 1.21.0 is now available. The highlights of the release are:
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continued SIMD work covering more functions and platforms, initial work on the new dtype infrastructure and casting, universal2 wheels for Python 3.8 and Python 3.9 on Mac, improved documentation, improved annotations, new PCG64DXSM bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people.</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>2021 NumPy survey July 12, 2021 &ndash; At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months.
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It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time.</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:
<ahref=/contributeid=navbar-itemclass="navbar-item is-secondary">Contribute</a></div></div></div></nav><sectionclass="content-padding flex-row"><divclass=shortcuts-container><divclass=shortcuts-title>Shortcuts</div><divid=shortcuts></div><divclass=sidebar-links><ahref=/gethelp>Get Help</a></div></div><divclass=content-container><divclass=columns><divclass="column is-centered-tablet-portrait"><h1class="title section-title">News</h1><h5class="subtitle is-5 is-muted"></h5><divclass=divider></div></div></div><h3id=2021-numpy-survey>2021 NumPy survey</h3><p><em>July 12, 2021</em> – At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months.</p><p>It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish.</p><p>Follow the link to get started: <ahref=https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q>https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q</a>.</p><h3id=numpy-1210-release>Numpy 1.21.0 release</h3><p><em>Jun 23, 2021</em> – <ahref=https://numpy.org/doc/stable/release/1.21.0-notes.html>NumPy 1.21.0</a>
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is now available. The highlights of the release are:</p><ul><li>continued SIMD work covering more functions and platforms,</li><li>initial work on the new dtype infrastructure and casting,</li><li>universal2 wheels for Python 3.8 and Python 3.9 on Mac,</li><li>improved documentation,</li><li>improved annotations,</li><li>new <code>PCG64DXSM</code> bitgenerator for random numbers.</li></ul><p>This NumPy release is the result of 581 merged pull requests contributed by 175 people.
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The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released.</p><h3id=2020-numpy-survey-results>2020 NumPy survey results</h3><p><em>Jun 22, 2021</em> – 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: <ahref=https://numpy.org/user-survey-2020/>https://numpy.org/user-survey-2020/</a>.</p><h3id=numpy-1200-release>Numpy 1.20.0 release</h3><p><em>Jan 30, 2021</em> – <ahref=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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