Python is the most popular programming language, known for its intuitive language and wide range of supported libraries. Because of its massive support, there is almost no limit to what you can do with the data you gather.
Some recommended libraries for web scraping include:
A tool for parsing the HTML and XML of a webpage. BeautifulSoup can create trees that reveal the content of the parsed pages. It can help you to identify extra specific elements from a page.
This library enables you to make HTTP requests without adding query strings, making the process simpler and easier.
Built to test web applications, Selenium scrapes the web. It can be used to automate website access and scrape dynamic pages that utilize Javascript.
Scrapy is a tool for web scraping and crawling, Scrapy lets you design “spiders” to collect URLs and follow links to a depth you set. Documentation.
For more information on using these four libraries, visit the guide on ScrapingDog.com
Are you interested in trying web scraping with Python but don't know where to start? Try our Google Colab notebook for a walkthrough on using Requests and BeautifulSoup to parse the HTML of a page and store scraped data in a table.
After gathering data with web scraping, you can manipulate and visualize that data within Python. Here are some suggested Python libraries for managing and interpreting your data:
A popular library for managing and manipulating data in Python. It works with tabular data such as CSV, JSON, XLS, and SQL to create organized data frames. These data frames can then be visualized to share and interpret data.
An alternative to Pandas for working with data in Python. It has the advantage of utilizing all of a computer's cores to work with large datasets more quickly and efficiently than Pandas. However, it is a newer and more complicated library so you should consider the size of your data and comfort with Python when choosing between the two.
NumPy is essential for working with arrays and performing numerical calculations within Python. It can be used with Pandas data frames to manage quantitative data.
Natural Language Toolkit is the classic Python library for natural language processing tools. With it, you can perform tokenization, stemming, and text parsing, all essential when working with textual data.
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