Understand the characteristics of scrapy framework and improve crawler development efficiency

The Scrapy framework is an open source framework based on Python, mainly used to crawl website data. It has the following characteristics:

1. Asynchronous processing: Scrapy Using asynchronous processing, multiple network requests and data parsing tasks can be processed simultaneously, which improves the crawler's data capture speed.
2. Simplify data extraction: Scrapy provides powerful XPath and CSS selectors to facilitate users to extract data. Users can use these selectors to extract data from web pages quickly and accurately.
3. Modular design: The Scrapy framework provides many modules that can be freely matched according to needs, such as downloaders, parsers, pipes, etc.
4. Convenient expansion: The Scrapy framework provides a rich API that can easily expand the functions that users need.

The following will introduce how to use the Scrapy framework to improve the efficiency of crawler development through specific code examples.

First, we need to install the Scrapy framework:

pip install scrapy

Next, we can create a new Scrapy project:

scrapy startproject myproject

This will create a project called " myproject" folder, which contains the basic structure of the entire Scrapy project.

Let’s write a simple crawler. Suppose we want to get the movie title, rating and director information of the latest movie from the Douban movie website. First, we need to create a new Spider:

import scrapy

class DoubanSpider(scrapy.Spider):
    name = "douban"
    start_urls = [
        'https://movie.douban.com/latest',
    ]

    def parse(self, response):
        for movie in response.xpath('//div[@class="latest"]//li'):
            yield {
                'title': movie.xpath('a/@title').extract_first(),
                'rating': movie.xpath('span[@class="subject-rate"]/text()').extract_first(),
                'director': movie.xpath('span[@class="subject-cast"]/text()').extract_first(),
            }

In this Spider, we define a Spider named "douban" and specify the initial URL as the URL of Douban Movie's official latest movie page. In the parse method, we use the XPath selector to extract the name, rating, and director information of each movie, and use yield to return the results.

Next, we can make relevant settings in the project's settings.py file, such as setting User-Agent and request delay, etc.:

USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'
DOWNLOAD_DELAY = 5

Here we set up a User-Agent, And set the download delay to 5 seconds.

Finally, we can start the crawler from the command line and output the results:

scrapy crawl douban -o movies.json

This will start the Spider we just created and output the results to a file called "movies.json" middle.

By using the Scrapy framework, we can develop crawlers quickly and efficiently without having to deal with too many details of network connections and asynchronous requests. The powerful functions and easy-to-use design of the Scrapy framework allow us to focus on data extraction and processing, thus greatly improving the efficiency of crawler development.

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