What are the commonly used python crawler libraries

Python has many commonly used crawler libraries. The following are some of the common crawler libraries:

1. Requests：

is a simple and easy-to-use HTTP library for sending HTTP requests and handling responses. It provides a concise API to handle HTTP requests such as GET, POST, and automatically handles cookies, session management, etc.

2. BeautifulSoup：

is an HTML parsing library for extracting and parsing data in HTML documents. It provides a simple and flexible way to traverse and search elements of HTML documents and extract the required data.

3. Scrapy：

Is a powerful high-level web crawler framework. It provides a structured, highly configurable and extensible framework for the entire crawler process, including functions such as request sending, page parsing, data extraction, and data processing.

4. Selenium：

is an automated testing tool that can also be used for web scraping. It can simulate browser behavior, including clicking, filling in forms, executing JavaScript, etc. It is suitable for dynamic web pages and scenarios that need to simulate user interaction.

5. Scrapy-Redis：

It is an extension of the Scrapy framework to support distributed crawlers and integration with Redis. It provides functions such as distributing crawler tasks to multiple nodes, passing URLs through Redis queues, etc., so that crawlers can run and manage more efficiently.

In addition to the libraries listed above, there are many other Python libraries for crawlers, such as libraries for crawling web pages (such as Urllib, httplib2), libraries for page parsing (such as lxml, pyquery), libraries for anti-crawler processing (such as Faker, proxies), and libraries for data processing and storage (such as Pandas, SQLite), etc.

Which crawler library is used the most

Currently the most commonly used Python crawler library is Scrapy. Scrapy is a powerful and widely used advanced web crawler framework, which is widely used in crawler projects of all sizes. Following are some of the main advantages of Scrapy:

1. Complete crawler process control: Scrapy provides complete crawler process control, including request sending, page parsing, data extraction, and data processing. The entire process can be controlled by defining Spider classes and writing parsing rules.

2. Highly configurable and extensible: Scrapy uses a flexible component architecture, allowing users to easily customize and extend various parts. For example, request handling, data storage pipelines, middleware, etc. can be customized.

3. Asynchronous processing and multi-thread support: Scrapy supports asynchronous processing, which can efficiently send and process HTTP requests. At the same time, it also supports multi-threading, which can process multiple requests and page parsing in parallel to speed up crawling.

4. Distributed crawling support: Scrapy-Redis extension enables Scrapy to support distributed crawlers, and realize efficient distributed crawling and data processing by distributing crawler tasks to multiple nodes.

5. Built-in data storage and export functions: Scrapy provides a variety of data storage methods, such as storing in JSON, CSV, XML and other formats, or directly storing in the database. At the same time, you can also customize the data storage pipeline to facilitate data processing and export.

Scrapy sample code

The following is a simple Scrapy sample code for crawling the title and links of a website:

1. Create a new Scrapy project:

scrapy startproject myproject
cd myproject

2. Create a Spider (such as spiders/myspider.py):

import scrapy

class MySpider(scrapy.Spider):
    name = 'myspider'
    start_urls = ['http://www.example.com']

    def parse(self, response):
        for article in response.css('article'):
            title = article.css('h2 a::text').get()
            link = article.css('h2 a::attr(href)').get()
            yield {
                'title': title,
                'link': link,
            }

This Spider will start crawling from the specified `start_urls` list and use the `parse` method to process the response. In this example, we use CSS selectors to extract the title and link, and return the data via the `yield` keyword.

3. Run the crawler:

scrapy crawl myspider -o output.json

Running the above command will start the crawler and save the results to a file named `output.json`.

This is just a simple example, and you can perform more complex data extraction and processing according to actual needs and the structure of the website. Scrapy provides powerful functions and rich extension mechanisms, which you can customize and extend according to project requirements.

Summarize

Although Scrapy is currently the most commonly used crawler library, the specific choice still depends on your needs and specific application scenarios. Sometimes other libraries like Requests and BeautifulSoup may be better suited for simple scraping tasks. Therefore, it is very important to choose the most suitable library according to the specific requirements of the project.

Which library is used most by python crawlers