Getting started with Web Scraping in Python
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The document explains web scraping as a method for extracting large volumes of data from websites into local files, emphasizing its utility for various applications. It details the three main steps of web scraping: getting content, parsing the response, and preserving the data, while outlining tools and libraries available like BeautifulSoup and Scrapy. Additionally, it addresses challenges, ethical considerations, and offers examples of practical applications, stressing the importance of conforming to a site's terms of use.
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๋๋ค!
๋ค์ด๋ก๋ ๋ฐ์ผ์๋ฉด ๊ณ ํ์ง๋ก ๋ณผ ์ ์์ต๋๋ค!! :)[236] แแ
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[236] ์นด์นด์ค์๋ฐ์ดํฐํ์ดํ๋ผ์ธ ์ค๋์Machine learning
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Getting started with Web Scraping in Python
- 1. Scrapingtotherescue (Webscrapingusingpython) By : Satwik Kansal and Pradhvan Bisht
- 2. Whatiswebscraping ? Web scraping is a technique to extract large amounts of data from websites whereby the data is extracted and saved to a local file in your computer. The data can be used for several purposes like displaying on your own website and application, performing data analysis or for any other reason.
- 4. whyshouldyouscrape - API may not provide what you need - No rate limit - Take what you really want! - Reduces manual effort - Swag!
- 6. Howitโsdone? Broadly a Three Step Process 1. Getting the content (in most cases HTML) 2. Parsing the response. 3. Optimizing/Improving the performance and preserving the data
- 7. GETTINGTHECONTENT โ Using modules like urllib, urllib2, requests, mechanize and selenium. โ Involves GET/POST request to the server. โ The response contains the information to be extracted. โ Sometimes not as easy as it may seem.
- 8. ExtractingTheData 1. Using Regular Expression and Basic python Tricky, complex and kind of fragile. 2. Using Parsing Libraries โ Two different approaches possible -- Simple Parsing and Search Tree parsing. โ Some popular libraries are BeautifulSoup, Lxml, and html5lib. โ Each modules has its own techniques and thus its own pros and trade- offs
- 11. PreservingTheData 1. Writing to a file. 2. Exporting as csv or excel file. 3. Storing in a database.
- 12. Examples Example 1 : Scraping Tweets from Twitter using BeautifulSoup and pythonโs Requests module Code Example 2 : Scraping top Stackoverflow posts using Scrapy Code Example 3 : Using Selenium to Log in and fetch library details from a university library site which uses Dynamic HTML.
- 14. WHATTOUSEWHERE 1. Handling dynamically generated html Solutions: Selenium or Spidermonkey 2. Cookie based Authentication Solution : Requests module. 3. Simple scraping Solutions: BeautifulSoup+Requests, Scrapy, Selenium
- 16. Scrapinghacks 1. Overcoming captchas Lookup tables, One time manual entry , Death By Captchas (paid service) 2. Per IP address query limit Using tsocks, ssh_D and socks monkey. 3. Improving performance Multiprocessing , gevent and requests.async() method.
- 17. Example3 Automating My College Library Problems : 1. Authentication 2. Dynamically Generated <iframe> tag Solution Selenium with headless Browser like PhantomJS Alternative: Mechanize Code
- 19. EthicsOfScraping Exceeding authorized use of the site Means doing anything that is prohibited in the Terms of Use (See CFAA, breach of contract, unjust enrichment, trespass to chattels, and various state laws similar to CFAA) Copyright Issues If the material you are scraping is not factual, but something that required some amount of creativity to create, you have copyright to worry about. QuickTip -- Conform to the the robots.txt file.
- 21. โ The brute-force way to get the information required. โ Absolutely Legal โ Not always that easy.