Best Python Libraries for Web Scraping

        Explore a range of powerful Python libraries for web scraping, including libraries for HTTP requests, parsing HTML/XML, and automated browsing.

        Web scraping has become an indispensable tool in today's data-driven world. Python is one of the most popular scraping languages, with a huge ecosystem of powerful libraries and frameworks. In this article, we'll explore the best Python libraries for web scraping, each offering unique features and capabilities to simplify the process of extracting data from websites.
        This article will also cover the best libraries and best practices to ensure efficient and responsible web scraping. From respecting site policies and processing rate limits to resolving common challenges, we'll provide valuable insights to help you effectively navigate the world of web scraping.
Scrape-It.Cloud
        Let's start with the Scrape-It.Cloud library, which provides access to an API for scraping data. This solution has several advantages. For example, we do this through an intermediary rather than scraping data directly from the target website. This ensures we won't be blocked when scraping large amounts of data, so we don't need a proxy. We don't have to solve the captcha problem because the API will take care of that. Also, we can crawl both static and dynamic pages.

feature

With the Scrape-It.Cloud library, you can easily extract valuable data from any site with simple API calls. It solves problems with proxy servers, headless browsers and captcha solving services. 

By specifying the correct URL, Scrape-It.Cloud can quickly return JSON with the necessary data. This allows you to focus on extracting the right data without worrying about data being blocked.

Additionally, this API allows you to extract data from dynamic pages created with React, AngularJS, Ajax, Vue.js, and other popular libraries.

Also, if you need to collect data from Google SERP, you can also use this API key for serp api python library.

installing

To install the library, run the following command:

pip install scrapeit-cloud

To use the library, you also need an API key. You can get it by registering on the site. Plus, you'll get some free credits to make pull requests and explore the library's features for free.

Example of use

A detailed description of all the functionality, features, and usage of a particular library deserves its own article. For now, we'll just show you how to get the HTML code for any webpage, whether you have access to it, whether you need a captcha solution, and whether the page content is static or dynamic.

To do this, just specify your API key and page URL.

from scrapeit_cloud import ScrapeitCloudClient
import json
client = ScrapeitCloudClient(api_key="YOUR-API-KEY")
response = client.scrape(
    params={
  "url": "https://example.com/"
}
)

Since the result is in JSON format and the content of the page is stored in a property ["scrapingResult"]["content"], we'll use this to extract the required data.

data = json.loads(response.text)
print(data["scrapingResult"]["content"])

As a result, the HTML code of the retrieved page will be displayed on the screen.

Combination of Requests and BeautifulSoup

One of the simplest and most popular libraries is BeautifulSoup. However, keep in mind that it's a parsing library and doesn't have the ability to make requests on its own. As such, it is often used with simple requests libraries such as Requests, http.client or cUrl.
 

feature

This library is designed for beginners and very easy to use. Plus, it has well-documented instructions and an active community.
The BeautifulSoup library (or BS4) is specifically designed for parsing, which gives it a wide range of capabilities. You can crawl web pages using XPath and CSS selectors.
Due to its simplicity and active community, numerous examples of its use are available online. In addition, if you encounter difficulties during use, you can get help to solve your problems.

installing

As mentioned before, we need two libraries to use it. To handle requests, we'll use the Requests library. The good news is that it comes pre-installed, so we don't need to install it separately. However, we do need the BeautifulSoup library installed to use it. To do this, simply use the following command:

pip install beautifulsoup4

Once installed, you can start using it right away.

Example of use

Suppose we want to retrieve <h1>the content of a tag that includes a header. To do this, we first need to import the necessary libraries and make a request to get the content of the page:

import requests
from bs4 import BeautifulSoup

data = requests.get('https://example.com')

To process the page, we'll use the BS4 parser:

soup = BeautifulSoup(data.text, "html.parser")

Now, all we have to do is specify the exact data we want to extract from the page:

text = soup.find_all('h1')

Finally, we display the acquired data on the screen:

print(text)

As we can see, using this library is very simple. However, it does have its limitations. For example, it can't scrape dynamic data because it's a parsing library that works with the basic requests library instead of a headless browser.

LXML

LXML is another popular data parsing library, which cannot be used for scraping alone. Since it also requires a library to make requests, we'll use the familiar Requests library we already know.

feature

Although it is similar to the previous library, it does provide some additional functionality. For example, it is better at handling XML document structures than BS4. Although it also supports HTML documents, if you have more complex XML structures, this library will be a more suitable choice.

installing

As mentioned earlier, although the requests library is required, we only need to install the LXML library since the other required components are already pre-installed.

To install LXML, enter the following commands at the command prompt: ,

pip install lxml

Now let's move on to an example of using the library.

Example of use

First, like last time, we need to use a library to get the HTML code of the web page. This part of the code is the same as the previous example.

Example of use

Unlike the library example, creating projects, like spider files, is done with special commands. It must be entered on the command line.

First, let's create a new project in which to build our crawler. Use the following command:

scrapy startproject test_project

 

Before we move on to creating the spider, let's take a look at the structure of the project tree. 

The files mentioned here are automatically generated when creating a new project. Any settings specified in these files will be applied to all spiders in the project. You define public classes in the "items.py" file, specify what to do when the project starts up in the "pipelines.py" file, and configure general project settings in the "settings.py" file.

Best Practices and Considerations

To make web scraping efficient, there are a few rules to follow. Following these rules helps make your crawlers more efficient and ethical, and reduces the load on the services you collect information from. 

avoid excessive requests

During web scraping, avoiding excessive requests is important to prevent getting blocked and reduce the load on the target website. That's why it is recommended to collect data from the website during the least busy hours, such as evenings. This helps reduce the risk of overloading resources and causing them to fail. 

Handle dynamic content

In the process of collecting dynamic data, there are two methods. You can do the scraping yourself using a library that supports headless browsers. Alternatively, you can use the web scraping API, which will handle the task of collecting dynamic data for you.

If you have good programming skills and a small project, you might be better off writing your own scraper using a library. However, if you are a beginner or need to collect data from multiple pages, a web scraping API is better. In this case, in addition to collecting dynamic data, the API will also be responsible for proxying and solving captchas.

Conclusions and main points

This article discusses libraries for web scraping and the following rules. To summarize, we created a table and compared all the libraries we covered.

The comparison table below highlights some key features of Python libraries for web scraping:

library	Analysis ability	Advanced Features	JS rendering	easy to use
Scrape-It.Cloud	HTML、XML、JavaScript	Automatic fetching and pagination	Yes	simple
Combination of requests and BeautifulSoup	HTML、XML	easy integration	No	simple
Requests and LXML Combination	HTML、XML	XPath and CSS selector support	No	ease
Gua Sha	HTML、XML	multiple spiders	No	ease
selenium	HTML、XML、JavaScript	Dynamic Content Handling	Yes (use network driver)	ease
Pipit Division	HTML、JavaScript	Browser Automation Using Headless Chrome or Chromium	Yes	ease

Overall, Python is a very useful programming language for data collection. With its wide range of tools and user-friendly nature, it is commonly used for data mining and analysis. Python can easily perform tasks related to extracting information from websites and manipulating data.