Scrapy implements data crawling for keyword search

Crawler technology is very important for obtaining data and information from the Internet, and scrapy, as an efficient, flexible and scalable web crawler framework, can simplify the process of data crawling and is very useful for crawling data from the Internet. practical. This article will introduce how to use scrapy to implement data crawling for keyword searches.

1. Introduction to Scrapy

Scrapy is a web crawler framework based on the Python language. It is efficient, flexible and scalable and can be used for data crawling, Various tasks such as information management and automated testing. Scrapy contains a variety of components, such as crawler parsers, web crawlers, data processors, etc., through which efficient web crawling and data processing can be achieved.

2. Implementing keyword search

Before using Scrapy to implement data crawling for keyword search, you need to know something about the architecture of the Scrapy framework and basic libraries such as requests and BeautifulSoup. learn. The specific implementation steps are as follows:

(1) Create a project

Enter the following command on the command line to create a Scrapy project:

scrapy startproject search

This command will create a directory named search in the current directory, which contains a settings.py file and a subdirectory named spiders.

(2) Crawler writing

Create a new file named searchspider.py in the spiders subdirectory, and write the crawler code in the file.

First define the keywords to be searched:

search_word = 'Scrapy'

Then define the URL for data crawling:

start_urls = [

'https://www.baidu.com/s?wd={0}&pn={1}'.format(search_word, i*10) for i in range(10)

]

This code will crawl data from the first 10 pages of Baidu search results.

Next, we need to build a crawler parser, in which the BeautifulSoup library is used to parse the web page, and then extract information such as the title and URL:

def parse(self , response):

soup = BeautifulSoup(response.body, 'lxml') for link in soup.find_all('a'): url = link.get('href') if url.startswith('http') and not url.startswith('https://www.baidu.com/link?url='): yield scrapy.Request(url, callback=self.parse_information) yield {'title': link.text, 'url': url}

The BeautifulSoup library is used when parsing web pages. This library can make full use of the advantages of the Python language to quickly parse web pages and extract the required data.

Finally, we need to store the captured data in a local file and define the data processor in the pipeline.py file:

class SearchPipeline(object):

def process_item(self, item, spider): with open('result.txt', 'a+', encoding='utf-8') as f: f.write(item['title'] + ' ' + item['url'] + '

')

This code processes each crawled data and writes the title and URL to the result.txt file respectively.

(3) Run the crawler

Enter the directory where the crawler project is located on the command line, and enter the following command to run the crawler:

scrapy crawl search

Use this command to start the crawler program. The program will automatically crawl data related to the keyword Scrapy from Baidu search results and output the results to the specified file.

3. Conclusion

By using basic libraries such as Scrapy framework and BeautifulSoup, we can easily implement data crawling for keyword searches. The Scrapy framework is efficient, flexible and scalable, making the data crawling process more intelligent and efficient, and is very suitable for application scenarios where large amounts of data are obtained from the Internet. In practical applications, we can further improve the efficiency and quality of data crawling by optimizing the parser and improving the data processor.

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