BeautifulSoup is a Python library that helps with scraping information from websites by providing tools to extract data from HTML and XML files

BeautifulSoup

Scrapy is a Python framework that is used to create web crawlers. These web crawlers can extract structured data directly from web sites or by using APIs. 

Scrapy

The re library allows the user to perform matches with regular expressions.

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University of Michigan - Ann Arbor

Python's data science libraries can be broken down into three different categories:
- Data Mining
- Data Processing and Modeling
- Data Visualization

Categorizing Python Libraries for DS

https://www.dataquest.io/blog/15-python-libraries-for-data-science/

Python Libraries for Data Science

Data Mining Libraries

Data Processing and Modeling

•Data mining: Obtaining data that can be used to deduce conclusions.
•Data processing and modeling: Cleaning extracted data in order to prepare it for analysis and processing this cleaned data in order to reach conclusions. 
•Data visualization: Visually depicting data in various forms allowing for easy interpretation of conclusions.



Defining the Different Library Types for Data Science

The rapid development of Transformers have brought a new wave of powerful tools to natural language processing. These models are large and very expensive to train, so pre-trained versions are shared and leveraged by researchers and practitioners.    
  
Hugging Face offers a wide variety of pre-trained transformers as open-source libraries, and you can incorporate these with only one line of code.  

The Transformer in NLP is a novel architecture that aims to solve sequence-to-sequence tasks while handling long-range dependencies with ease. The Transformer was proposed in the paper Attention Is All You Need. It is recommended reading for anyone interested in NLP.

Hugging Face transformers

Data visualization tools, such as seaborn, Bokeh, and matplotlib, are essential for manually inspecting datasets. By plotting the data, practitioners can visually identify underlying data quality issues—like extreme outliers or recording errors—and develop an intuition for the type of problems they may need to address before feeding data into a model.

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