</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/install/ class=navbar-item>Português

</a><a href=/ja/install/ class=navbar-item>日本語 (Japanese)</a></div></div></div></div></div></nav><section class=content-padding><div class=content-container><ul id=breadcrumbs class=bd-breadcrumbs><li class="breadcrumb-item breadcrumb-home"><a href=/><i class="fas fa-home"></i></a></li><li class=breadcrumb-item><a href=/install/>Installing NumPy</a></li></ul><h1>Installing NumPy</h1><div><p>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

Python, NumPy, and many other commonly used packages for scientific computing

and data science.</p><p>NumPy can be installed with <code>conda</code>, with <code>pip</code>, with a package manager on

macOS and Linux, or <a href=https://numpy.org/devdocs/building>from source</a>.

operating system of interest. If you&rsquo;re in between &ldquo;beginning&rdquo; and &ldquo;advanced&rdquo;,

please go with &ldquo;beginning&rdquo; if you want to keep things simple, and with

&ldquo;advanced&rdquo; if you want to work according to best practices that go a longer way

packages you need and all other tools mentioned below).</li><li>For writing and executing code, use notebooks in

<a href=https://jupyterlab.readthedocs.io/en/stable/index.html>JupyterLab</a> for

exploratory and interactive computing, and