In this blog we will discuss about the Best web scraping using python libraries and tools including Request, Selenium, lxml & Scrapy. You would seldom hear a word in the parlance of data science that there is enough data. With the large volume of data being added to the network every day, filtering out the data and extracting useful information from it becomes a challenging task. Thankfully there is a stack of best web scraping tools python available for data scientist helps them assess this data.
Web scraping is a powerful way of extracting useful information, and as a data scientist, if you have the knowledge of the best web scraping library for your application, then you stand in good stead in your interview.
Data is playing a significant role in changing business strategies, and hence, having the expertise in this domain will help you excel as a data scientist. To ensure accurate interpretation of the data, it is important to know the right tools to extract the data. Python web scraping tools for data scientists are going to help you in this.
Why rely on Python web scraping libraries?
When it comes to programming language, then Python takes the lead. It is one of the most powerful programming languages and a highly versatile one. It’s a universal application, which makes it a popular choice for developers.
One of its popular uses is web scraping, where the data is extracted from the portals. There are a host of python libraries available, but all of them may not be good for your project or purpose.
Automation- One of the primary reasons for using Python libraries is that it automates the entire process, thus making it a highly productive tool. When it comes to coding, it is to be done just once. Web scraper backed by Python libraries automatically extracts the data from the websites.
Combination- There are a few tools in Python libraries that don’t run very fast, and in such cases, you may need to combine them with others. You can use a single Python web scraping that can handle all the functions. Moreover, a web scraper built on Python is used to extract data, and parse and import it. It also gives you the leverage to see the visual depiction of the extracted data using Matplotlib.
In this article, we will be touring through some of the popular python libraries available in the market, along with their pros and cons.
Get the Best web scraping using python libraries
It is one of the most elemental Python libraries. This library functions by making an HTML request to the server of the website to retrieve data present on the page. This HTML content is the starting step of web scraping. It also makes HTTP requests like POST, GET, etc.
Positives of Request library
- It is easy to use tool
- It helps in extracting data from the website’s URL
- With the Request library, you can post, delete and update the data for a particular website
- It has an authentic module, so it is highly secure
- You can use it only for retrieving the data from a static webpage
- You cannot use it for parsing HTML
2. lxml Library
This is an extremely fast and high-performing library and covers up the drawbacks of the Request library. This library offers a blend of features from Element trees and Python. It is used to scrape large volumes of data.
<p”>Many a time, Request and lxml are used for web scraping of large data volume. You can extract data using XPath and CSS selectors.
Positives of lxml library
- These work at a blazingly high speed
- High-performing library
- Used for scraping large volumes of data
- It uses a blend of Element tree and Python
- It cannot be used with poorly designed HTML
- The documentation of this library is not beginner-friendly
If you have to pick the best Python library, then Scrapy is the right option. It is not just a library but a complete framework. It completely takes charge of web scraping.
This library is equipped with spider bots that can easily crawl on different websites to extract data. In addition, this library also leverages the creation of spider bots, which you can host as an API. In fact, you can create a pipeline of spider bots and use them later. And all this takes just a few minutes.
Positives of Scrpay
- It can be used to extract data from dynamic websites
- It has excellent documentation
- You can create spider bots using this library
- It has a well-designed architecture
- Not particularly for the beginners
This is yet another popularly used tool for extracting data. It helps in creating a parse tree for parsing HTML and XML documents. One of the defining features of this library is that it automates the conversion of incoming documents to Unicode and outgoing documents to UTF-8.
This library is easy to use and hence suitable for beginners. It can also be combined with lxml. However, you may have to compromise on the speed. If you have to deal with poorly designed HTML, then you can use this library.
Positives of Soup library
- You don’t need too many codes to use this
- Good documentation
- Easy to learn
- It automatically detects the encoding
- It’s not a fast tool
While most of the libraries mentioned above are good for non-dynamic websites, you can use the Selenium library if you want to extract data from a dynamically populated website. Initially, this library was used for testing web applications. But later, it found usefulness in extracting data from a dynamic website.
Positives of Selenium
- It can be used for scraping dynamic websites
- Automated web scrpaing
- It also automates the web browser
- You have to compromise on the speed
- You need a high-powered CPU and memory usage
- Its slow speed makes it unsuitable for large projects.
Wrapping it up !!!
From the above discussion, we can conclude that Python is the best language for web scraping. The primary reason for the use of the Python library is its high performance and simple syntax. The tools discussed here are the most powerful ones, but it is not just limited to these libraries; there are several other Python web scraping libraries available. Assessing project requirements will help in choosing the right Python library.
Hence knowledge and expertise in Python are paramount. Enrolling in Python programming also gives you the opportunity to learn these tools that can simplify your task.