Home Backend Development Python Tutorial Scrapy implements URL-based data crawling and processing

Scrapy implements URL-based data crawling and processing

Jun 23, 2023 am 10:33 AM
data processing url scrapy

With the increasing development of the Internet, a large amount of data is stored on web pages. These data contain a variety of useful information and can provide important basis for business decisions. How to obtain this data quickly and efficiently has become an urgent problem that needs to be solved. In crawler technology, Scrapy is a powerful and easy-to-use framework that can help us implement URL-based data crawling and processing.

Scrapy is an open source web crawler framework based on Python. It is a framework designed specifically for crawling data and has the advantages of being efficient, fast, scalable, easy to write and maintain. With the help of Scrapy, we can quickly obtain information on the Internet and transform it into useful data for our business. Below we will discuss how to use Scrapy to implement URL-based data crawling and processing.

Step One: Install Scrapy
Before using Scrapy, we need to install Scrapy first. If you have installed Python and the pip package management tool, enter the following command on the command line to install Scrapy:

pip install scrapy

After the installation is complete, we can start using Scrapy .

Step 2: Create a Scrapy project
We need to create a Scrapy project first. You can use the following command:

scrapy startproject sc_project

This will be in the current directory Create a folder named sc_project and create some necessary files for the Scrapy project in it.

Step 3: Define data items
Data items are the basic units of encapsulated data. In Scrapy, we need to define data items first, and then parse the data on the web page into data items. We can use the Item class provided by Scrapy to implement the definition of data items. The following is an example:

import scrapy

class ProductItem(scrapy.Item):

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name = scrapy.Field()

price = scrapy.Field()

description = scrapy.Field()

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In this example, we define ProductItem data items, including name, price and description three attributes.

Step 4: Write a crawler program
In Scrapy, we need to write a crawler program to crawl the data on the web page. We can use the Spider class provided in Scrapy to write crawler programs. The following is an example:

import scrapy

class ProductSpider(scrapy.Spider):

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name = 'product_spider'

allowed_domains = ['example.com']

start_urls = ['http://example.com/products']

 

def parse(self, response):

    for product in response.css('div.product'):

        item = ProductItem()

        item['name'] = product.css('div.name a::text').extract_first().strip()

        item['price'] = product.css('span.price::text').extract_first().strip()

        item['description'] = product.css('p.description::text').extract_first().strip()

        yield item

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In this example, we first define the ProductSpider class and define name, Three attributes: allowed_domains and start_urls. Then in the parse method, we use the CSS selector to parse the web page, parse the data on the web page into data items, and yield the data items.

Step 5: Run the crawler program
After writing the crawler program, we need to run the program. Just run the following command on the command line:

scrapy crawl product_spider -o products.csv

This will run the ProductSpider crawler program we just wrote and save the crawled data to the products.csv file.

Scrapy is a powerful web crawler framework that can help us quickly obtain information on the Internet and transform it into useful data for our business. Through the above five steps, we can use Scrapy to implement URL-based data crawling and processing.

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