Python is very useful for web page scraping. Website scraping refers to data mining techniques used to extract information from websites.
According to experts, Python is an excellent choice for web scraper developers because it includes its own libraries designed specifically for this purpose. Python libraries include tools and services for various purposes, such as Numpy, Matplotlib, Pandas, and others. Thus, it is suitable for scraping web data and further manipulating the extracted web data.
Today we’ve chosen to bring you some of the best libraries and tools for Web Scraping this year according to Analytics Insight.
Top 10 Python Libraries and Tools for Web Scraping in 2023
Request is without a doubt the most popular Python library for handling HTTP requests. The application lives up to its tagline, HTTP for HumansTM. It supports a wide range of HTTP request types, from GET and POST to PATCH and DELETE. Not only that, but almost every aspect of a request, including headers and responses, is under your control. When it comes to web scraping, requests are usually associated with Beautiful Soup because other Python frameworks have built-in support for handling HTTP requests.
This library has been updated from the request library. The request library’s drawback of parsing HTML is eliminated by the LXML library. The LXML library can extract large amounts of data quickly while maintaining high performance and efficiency. Combining both requests and LXML is the most effective method for removing data from HTML.
BeautifulSoup is probably the go-to library for python web scraping tools because it is easier to use for both beginners and experts. The main benefit of using BeautifulSoup is that you don’t have to worry about bad HTML. BeautifulSoup and request are frequently combined in web scraping tools. The disadvantage is that it is slower than LXML. BeautifulSoup should be used in conjunction with the LXML parser. The Python command to install BeautifulSoup is “pip install BeautifulSoup”.
Scrapy is an open-source, collaborative framework for extracting data from websites. Scrapy is a fast high-level web crawling and scraping framework written in Python. It is essentially a framework for creating web spiders that crawl websites and extract data from them. Scrapy uses Spiders, which are user-defined classes, to scrape information from websites.
Selenium is a popular Python scraping library that can scrape dynamic web content. This library allows you to simulate dynamic website actions such as button clicks, form filling, and more. It can scrape dynamic web pages. The disadvantage of selenium is that it is slow. It is unable to obtain status codes.
urllib3 is a Python web scraping library that is dependent on other libraries. It uses a PoolManager instance (class), which is a response object that manages connection pooling and thread safety. It handles concurrency with PoolManager. But more complicated syntax than other libraries such as Requests; urllib3 cannot extract dynamic data.
The best feature of import.io is that it is a tool that can automatically check scraped data and perform QA audits at regular intervals. This feature can be used to avoid scraping any null or duplicate values. Data types that can be scraped include product details, rankings, reviews, Q&A, and product availability.
The best tool for scraping a large amount of public data from social media websites is a data streamer. DataStreamer allows you to integrate unstructured data with a single API. It helps feed data pipelines with over 56,000 pieces of content and 10,000 enrichments per second using DataStreamer.
A proxy is not a Python tool, but it is required for web scraping. As previously stated, web scraping must be done with caution because some websites do not allow you to extract data from their web pages. If you do, your local IP address will most likely be blocked. A proxy masks your IP address and makes you anonymous online to prevent this.