<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title>Bing: Numpy</title><link>http://www.bing.com:80/search?q=Numpy</link><description>Search results</description><image><url>http://www.bing.com:80/s/a/rsslogo.gif</url><title>Numpy</title><link>http://www.bing.com:80/search?q=Numpy</link></image><copyright>Copyright © 2026 Microsoft. All rights reserved. These XML results may not be used, reproduced or transmitted in any manner or for any purpose other than rendering Bing results within an RSS aggregator for your personal, non-commercial use. Any other use of these results requires express written permission from Microsoft Corporation. By accessing this web page or using these results in any manner whatsoever, you agree to be bound by the foregoing restrictions.</copyright><item><title>NumPy</title><link>https://numpy.org/</link><description>NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.</description><pubDate>Thu, 02 Apr 2026 10:18:00 GMT</pubDate></item><item><title>NumPy documentation — NumPy v2.4 Manual</title><link>https://numpy.org/doc/stable/</link><description>NumPy documentation # Version: 2.4 Download documentation: Historical versions of documentation Useful links: Home | Installation | Source Repository | Issue Tracker | Q&amp;A Support | Mailing List NumPy is the fundamental package for scientific computing in Python.</description><pubDate>Thu, 02 Apr 2026 16:23:00 GMT</pubDate></item><item><title>NumPy - Learn</title><link>https://numpy.org/learn/</link><description>Scientific Python Lectures Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. NumPy: the absolute basics for beginners NumPy tutorial by Nicolas Rougier Stanford CS231 by Justin Johnson NumPy User Guide Books Guide to NumPy by Travis E. Oliphant This is the first and free edition of the book.</description><pubDate>Thu, 02 Apr 2026 13:09:00 GMT</pubDate></item><item><title>NumPy - Installing NumPy</title><link>https://numpy.org/install/</link><description>The only prerequisite for installing NumPy is Python itself. If you don’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.</description><pubDate>Fri, 03 Apr 2026 11:21:00 GMT</pubDate></item><item><title>NumPy Documentation</title><link>https://numpy.org/doc/</link><description>Web Latest (development) documentation NumPy Enhancement Proposals Versions: NumPy 2.4 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 2.3 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 2.2 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 2.1 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF ...</description><pubDate>Fri, 03 Apr 2026 11:14:00 GMT</pubDate></item><item><title>NumPy: the absolute basics for beginners — NumPy v2.4 Manual</title><link>https://numpy.org/doc/stable/user/absolute_beginners.html</link><description>NumPy: the absolute basics for beginners # Welcome to the absolute beginner’s guide to NumPy! NumPy (Num erical Py thon) is an open source Python library that’s widely used in science and engineering. The NumPy library contains multidimensional array data structures, such as the homogeneous, N-dimensional ndarray, and a large library of functions that operate efficiently on these data ...</description><pubDate>Fri, 03 Apr 2026 11:14:00 GMT</pubDate></item><item><title>NumPy user guide — NumPy v2.4 Manual</title><link>https://numpy.org/doc/stable/user/</link><description>NumPy user guide # This guide is an overview and explains the important features; details are found in NumPy reference.</description><pubDate>Thu, 02 Apr 2026 21:02:00 GMT</pubDate></item><item><title>NumPy quickstart — NumPy v2.4 Manual</title><link>https://numpy.org/doc/stable/user/quickstart.html</link><description>The basics # NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. In NumPy dimensions are called axes. For example, the array for the coordinates of a point in 3D space, [1, 2, 1], has one axis.</description><pubDate>Fri, 03 Apr 2026 08:15:00 GMT</pubDate></item><item><title>NumPy reference — NumPy v2.4 Manual</title><link>https://numpy.org/doc/stable/reference/</link><description>NumPy reference # Release: 2.4 Date: December 21, 2025 This reference manual details functions, modules, and objects included in NumPy, describing what they are and what they do. For learning how to use NumPy, see the complete documentation. Python API #</description><pubDate>Thu, 02 Apr 2026 08:16:00 GMT</pubDate></item><item><title>NumPy</title><link>https://numpy.org/ja/</link><description>NumPyの高速で多機能なベクトル化計算、インデックス処理、ブロードキャストの考え方は、現在の配列計算におけるデファクト・スタ&gt;ンダードです。</description><pubDate>Thu, 02 Apr 2026 21:23:00 GMT</pubDate></item></channel></rss>