<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>File-Formats on Ghafoor's Personal Blog</title><link>http://ghafoorsblog.com/categories/file-formats/</link><description>Recent content in File-Formats 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>Thu, 16 Apr 2026 17:17:05 +0100</lastBuildDate><atom:link href="http://ghafoorsblog.com/categories/file-formats/index.xml" rel="self" type="application/rss+xml"/><item><title>File Formats</title><link>http://ghafoorsblog.com/courses/ibm/fullstack-content/fullstack-pcert/07-python-datascience/05-module/005-file-formats/</link><pubDate>Fri, 25 Jul 2025 06:51:38 +0000</pubDate><author>noreply@example.com (AG Sayyed)</author><guid>http://ghafoorsblog.com/courses/ibm/fullstack-content/fullstack-pcert/07-python-datascience/05-module/005-file-formats/</guid><description>&lt;p class="lead text-primary"&gt;
This document covers the essential file formats used in data science, such as CSV, JSON, and Excel. It explains their structure, how to read and write them in Python, and the advantages and limitations of each format for data storage and exchange.
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&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;File formats are fundamental to data science, enabling the storage, exchange, and analysis of data. Understanding the structure and use cases of different file formats is crucial for efficient data handling.&lt;/p&gt;</description></item></channel></rss>