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所在平台: Udemy |
课程主页: https://www.udemy.com/course/scraping-with-python/
课程评论：没有评论
课程名称：使用Python进行网页抓取：强大的Python抓取专业课程 课程概述：本课程专注于网页抓取的基础和高级技能。如果您对任何编程语言有基本了解，就可以参加本课程。课程内容涉及从网站提取数据。由于我们生活在信息技术时代，因此需要一种自动化解决方案来处理这一问题，而这个解决方案正是“网页抓取”。本课程使用“Python”作为编程语言，Python提供了一些非常有用的库来处理数据，例如Pandas、NumPy以及专门用于网页抓取的“Beautiful Soup”。选择Python的另一个原因是其在当今时代的重要性，几乎所有调查都将Python评选为全球增长最快的编程语言之一。为此，本课程将从基础开始，逐步讲解所有内容，然后会进行一些高级项目，例如从网站提取图片或在提取后对数据进行处理。最后，我们将学习如何将数据以结构化格式存储，以便能在多种应用中使用。因此，我们将把数据导出为CSV或Excel格式，便于他人查阅，或用于演示、数据分析等多个场合。
This course focuses on the basic as well as advanced level of web scraping. You are eligible to take this course if you just have a basic knowledge of any programming language.This course is all about extraction of data from websites. Since we are living in an era of information technologies, so there has to be an automated solution to this problem and yes, there is a solution to this problem and we call it "Web Scraping".We are using "python" as a programming language for this purpose. Python provides us some very useful libraries in order to deal with data. For example: Pandas or numpy and specially "Beautiful soup" for web scraping. One of the other reasons to use python for this tutorial is the importance of python in this era, as almost every survey nominates python as the fastest emerging programming language in world. So in order to keep ourselves updated with modern day technologies, we are also using python for this course as a programming language.In this course we will take a start from absolutely basic level and will try to tell you everything from scratch. And then once we are good with our fundamentals, we will be doing some advances level projects as well, such as extracting pictures from websites or manipulating the data after the extraction.And finally we will learn how to store our data in a structured format so that it becomes useful for many other objectives. So we will be exporting the data into CSV or excel format so that it will become readable for everyone or it can be used in presentations or data analysis processes or anywhere you want.
