<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Vector-Operations on Ghafoor's Personal Blog</title><link>http://ghafoorsblog.com/tags/vector-operations/</link><description>Recent content in Vector-Operations on Ghafoor's Personal Blog</description><generator>Hugo</generator><language>en</language><managingEditor>noreply@example.com (AG Sayyed)</managingEditor><webMaster>noreply@example.com (AG Sayyed)</webMaster><copyright>Copyright © 2024-2026 AG Sayyed. All Rights Reserved.</copyright><lastBuildDate>Fri, 31 Oct 2025 11:40:28 +0000</lastBuildDate><atom:link href="http://ghafoorsblog.com/tags/vector-operations/index.xml" rel="self" type="application/rss+xml"/><item><title>One Dimensional Numpy</title><link>http://ghafoorsblog.com/courses/ibm/fullstack-content/fullstack-pcert/07-python-datascience/04-module/005-numpy/</link><pubDate>Thu, 24 Jul 2025 13:18:28 +0000</pubDate><author>noreply@example.com (AG Sayyed)</author><guid>http://ghafoorsblog.com/courses/ibm/fullstack-content/fullstack-pcert/07-python-datascience/04-module/005-numpy/</guid><description>&lt;p class="lead text-primary"&gt;
Numpy is a foundational Python library for scientific computing, offering efficient array creation, indexing, slicing, and vector operations. This document covers basic usage, attributes, universal functions, and practical examples for data science and mathematical analysis.
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&lt;h2 id="introduction-to-numpy-and-nd-arrays"&gt;Introduction to Numpy and ND Arrays&lt;/h2&gt;
&lt;p&gt;Numpy provides powerful tools for scientific computing, including ND arrays for storing and manipulating data. Arrays are fixed in size and contain elements of the same type, enabling fast and memory-efficient operations.&lt;/p&gt;</description></item></channel></rss>