13 best Python Web Scraping libraries

The finest Python libraries for web scraping are those mentioned above. These libraries can extract vast amounts of data from numerous sources, and the data can then be applied to various projects.  

The internet is teeming with websites, with more being created by the minute. There are numerous methods for obtaining information from those web pages. You can copy-paste the data into a web browser or develop a script to automate the procedure. Internet scraping is a computerized method for collecting massive data from websites. Most of this information is unstructured in HTML format and is changed into structured information in a database or spreadsheet so that it may be used in many applications. There are numerous approaches to web scraping in Python. You can utilize various tools and approaches depending on the aim of your web scraping assignment. Of fact, there is no optimal Python package for web scraping, simply the one that is most appropriate for you.  

To transform this web scraping process into an easier one, we have carefully handpicked a set of libraries in Python.   

you-get- 

• It is a lightweight command line utility.  
• It can scrape out media content from the web.  
• Can also help in downloading non-HTML content like binary files. 

scrapy- 

• High-level package for the fast extraction of data. 
• Can perform data mining as well as monitoring and automated testing. 
• You can extract the data from web pages using XPath. 

requests-html- 

• Intuitive and simple HTML parsing. 
• Automatic following of redirects. 
• Connection–pooling and cookie persistence. 
• CSS selectors and X-path selectors are like JQuery. 

newspaper- 

• Inspired by requests and powered by lxml. 
• Specifically for extracting and curating articles. 
• It can easily detect languages and can auto-detect if no language is specified. 

portia- 

• Can perform web scraping without any knowledge of coding. 
• The data to be extracted can be identified by annotating a web page. 
• Portia can be run using Docker. 

pattern 

• Web mining module created using Python. 
• It has tools for data mining, natural language processing, Machine learning, and network analysis. 
• It can also perform sentiment analysis. 

autoscraper 

• It supports automatic web scraping more easily. 
• Compatible with Python3 and can be installed using PyPI or pip. 
• It learns scraping rules and returns similar elements. 

tweets_analyzer 

• Can analyze tweets posted and scrape the metadata.  
• Average tweet activity can be analyzed by the hour and day of the week.  
• The time zone, language set for the Twitter interface, and sources used to access Twitter can be scrapped. 

grab 

• A python framework for building web scrapers. 
• Complex asynchronous website crawlers can be built. 
• Uses request/response API built on top of urllib3 and lxml for a building network request. 

ruia 

• Powered by asyncio and is declaratively programmed.  
• Supports JavaScript and is extensible by middleware and plugins.  
• Web-scraping MicroFrame is used for crawling URLs. 

gdom- 

• Web parsing powered by GraphQL syntax and Graphene framework. 
• Gdom query can be generalized to any page by rewriting the query page. 
• It is specifically designed for traversing and scraping DOM. 

scrapy-cluster- 

• Scraping cluster made using Redis and Kafka.  
• Raw HTML and assets are crawled interactively.  
• Seed URLs are distributed among many waiting spider instances, with requests coordinated via Redis. 

gazpacho- 

• A modern web scraping library with zero dependencies. 
• The get function can be used to download raw HTML. 
• Parsing is enabled using the SOUP wrapper.