What are the best web scraping libraries and frameworks for data analytics?

My experience: - for anything static urllib is the fastest solution, - while if its an interactive/js-based site, then Selenium, as others have mentioned as well

Python libraries and frameworks for web scraping and data analytics include BeautifulSoup, Scrapy, and Selenium. These tools enable data extraction and analysis from web sources, facilitating robust data-driven insights.

Python web scraping libraries are as follows:- 1.Beautiful Soup: Parses HTML and XML, creating parse trees for easy data extraction. 2. Requests: Primarily an HTTP library, excellent for managing HTTP requests. 3. Scrapy: A robust web crawling framework, ideal for large-scale scraping across multiple sites concurrently. 4. Selenium: Interacts with web elements, useful for scraping dynamic content generated by JavaScript. 5. LXML: High-performance library for parsing XML and HTML documents in Python. 6. MechanicalSoup: Combines Requests and Beautiful Soup, facilitating form submissions and interaction with web pages.

Oracle recognizes Python's prowess in handling diverse data scraping tasks and acknowledges the significance of its libraries in facilitating this process. For instance, Oracle's integration of Python libraries like Requests aligns with the objective of streamlining basic web scraping tasks, offering a simple yet effective solution to access and retrieve data from various online source

Though R is very popular for text mining, for webscraping I prefer to use python and analyze the parsed data within R for statistical analysis. Web scraping is a heavy I/O bound tasks and requires a good response handling and tools like python and Ruby are well suited for these tasks with multi threading and asynchronous programming patterns

In R, there are several powerful libraries and packages for web scraping and data analysis. These tools are essential for extracting valuable information from online sources and conducting robust analyses. Some of the key packages in R for these tasks include rvest, httr, xml2, RSelenium, and tidyverse.

Other examples apart from Python and R include the following Node.js: JavaScript-based runtime environment, which offers various libraries for web scraping such as Cheerio, Puppeteer, and Request. PHP: PHP has libraries like Goutte and Simple HTML DOM Parser that facilitate web scraping. Java: Jsoup is a Java library that provides a convenient API for extracting and manipulating data from HTML. Ruby: Mechanize and Nokogiri are popular libraries in the Ruby ecosystem for web scraping tasks. Go (Golang): Go has libraries like Colly and GoQuery that can be used for web scraping purposes.

Another popular tool is Ruby on Rails. you can use the Nokogiri and Open-URI gems to easily parse and extract data from websites. Nokogiri is a Ruby gem that makes it easy to parse and search HTML documents. It is well documented and can be used along with variety of different Ruby gems to get ehe data you need, like using it with therubyracer gem to parse the JavaScript and get the data you need. Open-URI is a Ruby gem that allows you to open and read files from the web.