The practice of applying Scrapy to website structure analysis

With the popularity and development of the Internet, more and more websites are showing a high degree of complexity and diversity. In this context, website structure analysis is particularly important because it can help us better understand the internal structure and composition of the website, thereby providing more comprehensive and detailed support to relevant developers.

Scrapy is a Python framework for crawling web sites and extracting structured data. It is based on the twisted framework and handles requests asynchronously. Using the Scrapy framework to analyze website structure can allow us to better understand the structure and content of the website, and help us better collect and process data.

In this article, we will introduce the practice of applying website structure analysis in the Scrapy framework.

1. Installation and configuration of Scrapy

First, we need to install Scrapy. It is recommended to use pip for installation, that is, enter: pip install scrapy on the command line.

After the installation is complete, some configuration is required. It mainly includes setting up User-Agent and setting up crawler pipelines.

1. Set User-Agent

In the process of writing the crawler, we need to forge a User-Agent string to make the crawler look more like an ordinary browser instead of A crawler. The advantage of doing this is to avoid being blocked or restricted by the website.

The setting method is to add the following code to the settings.py file:

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

2. Set pipelines

pipelines are the pipelines for data processing in Scrapy. By passing the data obtained by the crawler to pipelines, the data is saved and processed in pipelines. In Scrapy, a crawler can have multiple pipelines, and different pipelines can perform different operations on the data.

Add the following code to the settings.py file:

ITEM_PIPELINES = {

'scrapy_test.pipelines.MyPipeline': 300,  

}

Among them, 300 represents the priority of the pipeline. In the pipeline used by Scrapy by default, the priority of saving data to CSV files is the highest, which is 500. We can set the priority of our own pipeline to be lower than 500 and higher than other default pipelines.

2. Use of Scrapy

After completing the installation and configuration of Scrapy, we can now start writing our crawler. In Scrapy, structural analysis and data extraction of the website are realized by writing two basic components: spider and items.

1. Writing Spider

In Scrapy, the crawler is one of the basic components. By writing crawler code, we can control the crawler's access and crawling process to achieve the desired results.

By creating a Spider class, inheriting scrapy.Spider, and then implementing the three attributes and methods of name, start_url, and parse in the class, you can easily write a crawler.

Code snippet:

import scrapy

class MySpider(scrapy.Spider):

name = 'myspider'  
start_urls = ['http://www.example.com']   

def parse(self, response):

yield {'text': response.css('title::text').get()} 

The start_urls is the URL that the Spider starts crawling, and parse is the processing method of the HTML content obtained before the Spider visits the start_url URL.

2. Writing Items

Items is another basic component of Scrapy, and its main function is for data extraction and structured processing.

By defining an Item class, similar to defining fields in a table, structured processing of data can be achieved.

Code snippet:

import scrapy

class MyItem(scrapy.Item):

title = scrapy.Field()   
link = scrapy.Field() 

The above code defines an Item class, including title and link two attributes. Each property is a scrapy.Field object, which defines the type of data obtained. After the crawling is completed, Scrapy will save the data into a Python dictionary. The key names correspond to the attributes stored in the Item, and the key values ​​correspond to the obtained data.

3. Result Analysis and Processing

After completing the writing of the crawler, we can view the obtained data results by running the crawler program.

If we need to store data in the database, we can further process the obtained data and save it to the database by writing an Item Pipeline.

Code snippet:

import pymongo

class MyPipeline(object):

def __init__(self):   
    self.client = pymongo.MongoClient()   
    self.db = self.client['mydb']   

def process_item(self, item, spider):

    self.db['mydb'].insert(dict(item))   
    return item 

In the above code, we connected to the MongoDB database through the pymongo library and saved the obtained data to the database.

Summary

Scrapy is a powerful crawler framework based on the Python language, which provides a complete crawling and data processing solution. In practice, Scrapy can easily implement structural analysis and data extraction of the website, allowing us to better understand the internal structure and composition of the website, thus providing greater support to relevant developers.

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