Actual crawler: code analysis, easy access to network data resources!

Introduction: There are a lot of valuable data resources in the network, and crawler technology provides us with an effective way to obtain these data. This article will introduce 5 practical crawler cases, and attach corresponding code analysis, so that you can quickly understand the application scenarios and implementation methods of crawlers. At the same time, in order to facilitate learning and further in-depth exploration, we provide hyperlinks to relevant resources for readers' reference.

Case 1: Web content crawling

Description: Crawl the content of the specified webpage and save it as a local file.

Code implementation (Python):

import requests

def get_web_content(url, filename):
    response = requests.get(url)
    if response.status_code == 200:
        with open(filename, 'w', encoding='utf-8') as f:
            f.write(response.text)
        print("网页内容已保存为：", filename)
    else:
        print("网页访问失败")

if __name__ == "__main__":
    url = "https://www.example.com"  # 替换为目标网页地址
    filename = "web_content.txt"
    get_web_content(url, filename)

Code analysis:

Use requeststhe library to send HTTP requests to get web page content.
If the response status code is 200, save the content as a local file.
If the access fails, a prompt message is output.

related resources:

Case 2: Image Downloader

Description: Crawl pictures from web pages and download and save them locally.

Code implementation (Python):

import requests

def download_image(url, filename):
    response = requests.get(url)
    if response.status_code == 200:
        with open(filename, 'wb') as f:
            f.write(response.content)
        print("图片已保存为：", filename)
    else:
        print("图片下载失败")

if __name__ == "__main__":
    img_url = "https://www.example.com/images/example.jpg"  # 替换为目标图片地址
    img_filename = "example.jpg"
    download_image(img_url, img_filename)

Code analysis:

Use requeststhe library to download pictures and save the binary data of pictures as local files.
If the download is successful, output the save path; otherwise, output a prompt message.

related resources:

Python os module documentation

Case 3: Data Acquisition and Storage

Description: Crawl the product information of the online mall and store the data in a CSV file.

Code implementation (Python):

import requests
import csv

def scrape_product_info(url, filename):
    response = requests.get(url)
    if response.status_code == 200:
        products = response.json()
        with open(filename, 'w', newline='', encoding='utf-8') as csvfile:
            fieldnames = ['id', 'name', 'price', 'rating']
            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
            writer.writeheader()
            for product in products:
                writer.writerow(product)
        print("商品信息已保存为：", filename)
    else:
        print("数据采集失败")

if __name__ == "__main__":
    api_url = "https://www.example.com/api/products"  # 替换为目标API接口地址
    csv_filename = "product_info.csv"
    scrape_product_info(api_url, csv_filename)

Code analysis:

Use requeststhe library to get the JSON data returned by the API interface.
After the data is parsed, it is stored as a CSV file, which is convenient for subsequent processing and analysis.

related resources:

Python csv module documentation

Case 4: Timing crawling

Description: Set up timed tasks to crawl and update data regularly.

Code implementation (Python):

import requests
import time

def crawl_and_update(url):
    while True:
        response = requests.get(url)
        if response.status_code == 200:
            # 处理数据更新逻辑
            print("数据更新成功")
        else:
            print("数据更新失败")
        time.sleep(3600)  # 每隔1小时执行一次

if __name__ == "__main__":
    target_url = "https://www.example.com/data"  # 替换为目标数据地址
    crawl_and_update(target_url)

Code analysis:

Use timethe library to set up timed tasks to crawl data every hour.
After updating the data, perform corresponding processing operations.

related resources:

Python time module documentation

Case 5: Selenium automated crawler

Description: Use Selenium to simulate browser behavior for data collection.

Code implementation (Python):

from selenium import webdriver

def scrape_with_selenium(url):
    options = webdriver.ChromeOptions()
    options.add_argument('--headless')  # 无头模式，不弹出浏览器窗口
    driver = webdriver.Chrome(options=options)
    driver.get(url)
    
    # 在这里进行页面解析和数据采集
    # ...

    driver.quit()
    print("数据采集完成")

if __name__ == "__main__":
    target_url = "https://www.example.com"  # 替换为目标网页地址
    scrape_with_selenium(target_url)

Code analysis:

Using Selenium to simulate the Chrome browser can be used to crawl dynamically rendered pages or websites that require interactive operations.
According to the actual situation, scrape_with_seleniumthe logic of page parsing and data collection can be added to the function.

related resources:

Selenium Documentation

Conclusion: This article introduces 5 practical crawler cases, and provides corresponding code analysis and related resource links. I hope these cases can help readers quickly get started with crawler technology, and use it flexibly in practice to obtain more valuable data resources. Please note that when crawling web data, please abide by the terms of use of the website and the Robots agreement to avoid unnecessary burden on the website and infringement of the rights of others.