Introduction: Crawlers are a powerful tool that can help us get all kinds of data from the Internet. In this blog, we will introduce three actual crawler cases, including crawling song information on music streaming platforms and making music recommendations or personalized recommendations, crawling movie information and reviews on movie websites and making movie recommendations or movie reviews Analysis, as well as crawling hotel information and user reviews from travel websites and performing travel planning or hotel review analysis. We will provide corresponding code examples to help readers understand and practice these crawler applications.
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Case 1: Music recommendation
In this case, we will crawl the song information of the music streaming platform and make music recommendations or personalized recommendations. Here is the corresponding code example:
import requests
from bs4 import BeautifulSoup
def crawl_music_data(url):
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
songs = soup.find_all('div', class_='song')
for song in songs:
title = song.find('h2').text
artist = song.find('span', class_='artist').text
# 进行音乐推荐或个性化推荐
# ...
print(f'Title: {title}')
print(f'Artist: {artist}\n')
# 使用示例
url = 'http://example.com/music'
crawl_music_data(url)
In this example, we use requeststhe library to send HTTP requests and use BeautifulSoupthe library to parse HTML pages. By analyzing the page structure, we found <div class="song">the tag where the song information is located.
We iterate over all song tags and extract the song title and artist name. In this example, we can further perform music recommendation or personalized recommendation, such as recommending similar songs based on the user's preferences or historical playback records.
Finally, we print the song's title and artist name.
Case 2: Movie recommendation and movie review analysis
In this case, we will crawl movie information and reviews from movie websites, and perform movie recommendation or movie review analysis. Here is the corresponding code example:
import requests
from bs4 import BeautifulSoup
def crawl_movie_data(url):
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
movies = soup.find_all('div', class_='movie')
for movie in movies:
title = movie.find('h2').text
rating = movie.find('span', class_='rating').text
# 进行电影推荐或影评分析
# ...
print(f'Title: {title}')
print(f'Rating: {rating}\n')
# 使用示例
url = 'http://example.com/movies'
crawl_movie_data(url)
tifulSoupLibrary to parse HTML pages. By analyzing the page structure, we found <div class="movie">the tags where the movie information is located.
We iterate over all movie tags and extract the movie title and rating. In this example, we can further perform movie recommendation or movie review analysis operations, such as recommending similar movies based on user preferences or historical viewing records, or analyzing the emotional tendency of movie reviews, etc.
Finally, we print the movie's title and rating.
Case 3: Tourism Planning and Hotel Evaluation Analysis
In this case, we will crawl hotel information and user reviews from travel websites, and conduct travel planning or hotel review analysis. Here is the corresponding code example:
import requests
from bs4 import BeautifulSoup
def crawl_hotel_data(url):
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
hotels = soup.find_all('div', class_='hotel')
for hotel in hotels:
name = hotel.find('h2').text
rating = hotel.find('span', class_='rating').text
# 进行旅游规划或酒店评价分析
# ...
print(f'Hotel: {name}')
print(f'Rating: {rating}\n')
# 使用示例
url = 'http://example.com/hotels'
crawl_hotel_data(url)
In this example, we also use requeststhe library to send HTTP requests and use BeautifulSoupthe library to parse HTML pages. By analyzing the page structure, we found <div class="hotel">the tab where the hotel information is located.
We loop through all the hotel tags and extract the hotel name and rating. In this example, we can further carry out travel planning or hotel evaluation analysis operations, such as recommending suitable hotels based on user needs and preferences, or analyzing the emotional tendency of user reviews, etc.
Finally, we print the hotel's name and rating.
Case 4: E-commerce price comparison
In this case, we will crawl the product information of the e-commerce website and perform price comparison or data analysis. Here is the corresponding code example:
import requests
from bs4 import BeautifulSoup
def crawl_product_info(url):
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
products = soup.find_all('div', class_='product')
for product in products:
name = product.find('h2').text
price = product.find('span', class_='price').text
# 进行价格比较或数据分析
# ...
print(f'Product: {name}')
print(f'Price: {price}\n')
# 使用示例
url = 'http://example.com/products'
crawl_product_info(url)
In this example, we also use requeststhe library to send HTTP requests and use BeautifulSoupthe library to parse HTML pages. By analyzing the page structure, we found <div class="product">the label where the product information is located.
We iterate through all product tags and extract the product name and price. In this example, we can further perform price comparison or data analysis operations, such as calculating the average price, finding the cheapest item, and so on.
Finally, we print the item's name and price.
Case 5: Login authentication crawling
Some websites require users to log in to access some sensitive information or specific pages. In order to achieve this goal, we can use requeststhe library combined with the relevant login authentication mechanism. Here's a simple example of how to simulate a login and crawl pages that require login:
import requests
def login(username, password):
login_url = 'http://example.com/login'
data = {
'username': username,
'password': password
}
session = requests.Session()
response = session.post(login_url, data=data)
if response.status_code == 200:
return session
else:
return None
def crawl_authenticated_page(session, url):
response = session.get(url)
if response.status_code == 200:
return response.text
else:
return None
# 使用示例
username = 'your_username'
password = 'your_password'
session = login(username, password)
if session:
url = 'http://example.com/authenticated'
content = crawl_authenticated_page(session, url)
print(content)
else:
print('Login failed!')
In this example, we first define a login()function that accepts username and password as parameters. Inside the function, we build the login request and requests.Session()create a session object using
Send a login request through post()the methods of the session object, sending the username and password as data to the login URL. If the login was successful, we return the session object; otherwise None.
Then, we define a crawl_authenticated_page()function that accepts as parameters the session object and the URL of the page that needs to be logged in to access. Inside the function, we use the session object's get()method to send a request to get the content of the page.
Finally, we call login()the function with the example username and password, and if the login is successful, we will continue to call crawl_authenticated_page()the function to crawl the pages that require login to access, and print the content. If the login fails, we will output a message indicating that the login failed.
By simulating login authentication, we can crawl pages that require login to access to achieve more diverse crawler applications.
Conclusion: This blog introduces three actual crawler cases, including crawling song information on music streaming platforms and performing music recommendations or personalized recommendations, crawling movie information and reviews from movie websites and performing movie recommendation or movie review analysis. As well as crawling hotel information and user reviews from travel websites and conducting travel planning or hotel review analysis. Through these cases, readers can understand the application of reptiles in different fields and learn how to use