Python 3 网络爬虫学习建议?

如题,题主python只是比较熟悉numpy和scipy、matplotlib这三个包,都是做科研的时候在用。最近心血来潮自己写了几个机器学习算法,然…
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用py3写爬虫的话,强力推荐这本书,应该是目前最系统最完善介绍python爬虫的书。可以去图灵社区买电子版。书的内容很新也很系统,从beautifulSoup,requests到ajax,图像识别,单元测试。比起绝大多数blog零散的教程要好的多,看完书后就可以去做些实战项目,这个时候可以去github上找类似的项目借鉴下。

英文版pdf:

pan.baidu.com/s/1nvQ1Kp

密码: 82m9(个人觉得英文版更好)

中文版pdf:

图灵社区 : 图书 : Python网络数据采集

(支持正版)

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更新:有评论说这本书内容比较浅,我表示赞同。但是对于新手来说,看完这本书,对于爬虫基础的应用与概念绝对有了初步的了解。其实国内有一本讲爬虫的好书,《自己动手写网络爬虫》,这本书除了介绍爬虫基本原理,包括优先级,宽度优先搜索,分布式爬虫,多线程,还有云计算,数据挖掘内容。只不过用了java来实现,但是思路是相同的。

送书来了。python变化很快,2018 有 Web Scraping with Python 2nd - 2018.pdf

Python Web Scraping Cookbook - 2018.pdf

Website Scraping with Python - 2018.pdf

精通Python爬虫框架Scrapy - 2018.pdf 等爬虫接触的书籍,可以参考下:



2018最佳人工智能数据采集(爬虫)工具书下载

Published: 二 06 十一月 2018 By andrew In python.
Python网络数据采集



Python网络数据采集 - 2016.pdf
本书采用简洁强大的Python语言,介绍了网络数据采集,并为采集新式网络中的各种数据类型提供了全面的指导。第 1部分重点介绍网络数据采集的基本原理:如何用Python从网络服务器请求信息,如何对服务器的响应进行基本处理,以及如何以自动化手段与网站进行交互。第 二部分介绍如何用网络爬虫测试网站,自动化处理,以及如何通过更多的方式接入网络。
Web Scraping with Python 2nd - 2018.pdf
github.com/REMitchell/p 2000左右星
讨论钉钉免费群21745728 qq群144081101 567351477
精通Python爬虫框架Scrapy - 2018.pdf


Scrapy是使用Python开发的一个快速、高层次的屏幕抓取和Web抓取框架,用于抓Web站点并从页面中提取结构化的数据。《精通Python爬虫框架Scrapy》以Scrapy 1.0版本为基础,讲解了Scrapy的基础知识,以及如何使用Python和三方API提取、整理数据,以满足自己的需求。
本书共11章,其内容涵盖了Scrapy基础知识,理解HTML和XPath,安装Scrapy并爬取一个网站,使用爬虫填充数据库并输出到移动应用中,爬虫的强大功能,将爬虫部署到Scrapinghub云服务器,Scrapy的配置与管理,Scrapy编程,管道秘诀,理解Scrapy性能,使用Scrapyd与实时分析进行分布式爬取。本书附录还提供了各种软件的安装与故障排除等内容。 本书适合软件开发人员、数据科学家,以及对自然语言处理和机器学习感兴趣的人阅读。

Learning Scrapy -2016.pdf 另有中文电子版本 因为版权已经在CSDN等网站下架,可以在qq群144081101等找到。
python3爬虫基础


在线教程
github.com/MorvanZhou/e 200 左右星
First web scraper
教程:first-web-scraper.readthedocs.io
github.com/ireapps/firs 200 左右星
Practical Web Scraping for Data Science -Best Practices and Examples with Python - 2018.pdf


github.com/Apress/pract 星级 低于100
This book provides a complete and modern guide to web scraping, using Python as the programming language, without glossing over important details or best practices. Written with a data science audience in mind, the book explores both scraping and the larger context of web technologies in which it operates, to ensure full understanding. The authors recommend web scraping as a powerful tool for any data scientist’s arsenal, as many data science projects start by obtaining an appropriate data set.
Starting with a brief overview on scraping and real-life use cases, the authors explore the core concepts of HTTP, HTML, and CSS to provide a solid foundation. Along with a quick Python primer, they cover Selenium for JavaScript-heavy sites, and web crawling in detail. The book finishes with a recap of best practices and a collection of examples that bring together everything you've learned and illustrate various data science use cases.
用Python写网络爬虫 第2版


《用Python写网络爬虫(第 2版》讲解了如何使用Python来编写网络爬虫程序,内容包括网络爬虫简介,从页面中抓取数据的3种方法,提取缓存中的数据,使用多个线程和进程进行并发抓取,抓取动态页面中的内容,与表单进行交互,处理页面中的验证码问题,以及使用Scarpy和Portia进行数据抓取,并在最后介绍了使用本书讲解的数据抓取技术对几个真实的网站进行抓取的实例,旨在帮助读者活学活用书中介绍的技术。
《用Python写网络爬虫(第 2版》适合有一定Python编程经验而且对爬虫技术感兴趣的读者阅读。


Python Web Scraping 2nd Edition - 2017.pdf
第一版中文 用Python写网络爬虫.pdf
github.com/kjam/wswp < 100星
Python Web Scraping Cookbook - 2018.pdf
下载


Python Web Scraping Cookbook is a solution-focused book that will teach you techniques to develop high-performance Scrapers, and deal with cookies, hidden form fields, Ajax-based sites and proxies. You'll explore a number of real-world scenarios where every part of the development or product life cycle will be fully covered. You will not only develop the skills to design reliable, high-performing data flows, but also deploy your codebase to Amazon Web Services (AWS). If you are involved in software engineering, product development, or data mining or in building data-driven products, you will find this book useful as each recipe has a clear purpose and objective.
Right from extracting data from websites to writing a sophisticated web crawler, the book's independent recipes will be extremely helpful while on the job. This book covers Python libraries, requests, and BeautifulSoup. You will learn about crawling, web spidering, working with AJAX websites, and paginated items. You will also understand to tackle problems such as 403 errors, working with proxy, scraping images, and LXML.
By the end of this book, you will be able to scrape websites more efficiently and deploy and operate your scraper in the cloud.
github.com/PacktPublish < 100星
Website Scraping with Python - 2018.pdf


仔细检查网站抓取和数据处理:以适合进一步分析的格式从网站提取数据的技术。您将查看要使用的工具,并比较它们的功能和效率。本书简明扼要专注于BeautifulSoup4和Scrapy,突出了常见问题,并提出了读者可以自行实施的解决方案。
您将看到如何单独或一起使用BeautifulSoup4和Scrapy以获得所需的结果。由于许多站点都使用JavaScript,因此您还将使用Selenium和浏览器模拟器来呈现这些站点。
在本书的最后,您将拥有一个完整的抓取应用程序来使用和重写以满足您的需求。
github.com/Apress/websi
Social Media Data Mining and Analytics - 2018.pdf


Harness the power of social media to predict customer behaviorand improve sales
Social media is the biggest source of Big Data. Because of this,90% of Fortune 500 companies are investing in Big Data initiativesthat will help them predict consumer behavior to produce bettersales results. Written by Dr. Gabor Szabo, a Senior Data Scientistat Twitter, and Dr. Oscar Boykin, a Software Engineer at Twitter,Social Media Data Mining and Analytics shows analysts how touse sophisticated techniques to mine social media data, obtainingthe information they need to generate amazing results for theirbusinesses. Social Media Data Mining and Analytics isn’t just anotherbook on the business case for social media. Rather, this bookprovides hands-on examples for applying state-of-the-art tools andtechnologies to mine social media – examples include Twitter,Facebook, Pinterest, Wikipedia, Reddit, Flickr, Web hyperlinks, andother rich data sources. In it, you will learn:
The four key characteristics of online services-users, socialnetworks, actions, and content The full data discovery lifecycle-data extraction, storage,analysis, and visualization How to work with code and extract data to create solutions How to use Big Data to make accurate customer predictions
Szabo and Boykin wrote this book to provide businesses with thecompetitive advantage they need to harness the rich data that isavailable from social media platforms.