Python crawler case analysis: five practical cases and code examples (it is enough to learn crawlers and read this article)

Introduction: Python crawler is a powerful tool that can help us grab data from web pages and perform various processing and analysis. In this blog, we will introduce five practical Python crawler cases, and provide corresponding code examples and analysis. Through these cases, readers can understand how to apply Python crawlers to solve different data acquisition and processing problems, so as to further improve crawler skills.

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Case 1: Crawling weather data

import requests
import csv

url = 'http://example.com/weather-api'
response = requests.get(url)

weather_data = response.json()

with open('weather_data.csv', 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerow(['Date', 'Temperature', 'Humidity'])

    for data in weather_data:
        writer.writerow([data['date'], data['temperature'], data['humidity']])

Code analysis: In this case, we use the requests library to send HTTP requests to get weather data and save the data to CSV files. First, we send a GET request to get a JSON response for the weather data. We then use the csv library to create a CSV file and write the data. By looping through the weather data, we write the date, temperature, and humidity of each piece of data to a CSV file.

Case 2: Crawl pictures and download them

import requests

url = 'http://example.com/image-gallery'
response = requests.get(url)

image_urls = ['http://example.com/image1.jpg', 'http://example.com/image2.jpg', 'http://example.com/image3.jpg']

for image_url in image_urls:
    image_response = requests.get(image_url)
    with open(image_url.split('/')[-1], 'wb') as file:
        file.write(image_response.content)

Code analysis: This case demonstrates how to crawl pictures on the website and download them locally. We send a GET request to get the page for the image link, and iterate through the list of image links. For each image link, we send a GET request to get the response of the image, and use the with open statement to open a file and write the contents of the image to the file.

Case 3: Crawling Movie Reviews

import requests
from bs4 import BeautifulSoup

url = 'http://example.com/movie-reviews'
response = requests.get(url)

soup = BeautifulSoup(response.content, 'html.parser')
reviews = soup.find_all('div', class_='review')

for review in reviews:
    title = review.find('h2').text
    content = review.find('p').text
    rating = review.find('span', class_='rating').text

    print('Title:', title)
    print('Content:', content)
    print('Rating:', rating)
    print('---')

Code Analysis: This case shows how to crawl movie reviews on movie websites and extract key information. We send a GET request to get the HTML response of the movie review page, and then use the BeautifulSoup library to parse the HTML response. Through the find_all method, we find div elements with class 'review', which contain movie reviews. For each movie review, we use the find method to find the title, content, and rating, and print them out.

Case 4: Crawling news articles and performing text analysis

import requests
from bs4 import BeautifulSoup
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist

url = 'http://example.com/news-articles'
response = requests.get(url)

soup = BeautifulSoup(response.content, 'html.parser')
articles = soup.find_all('article')

for article in articles:
    title = article.find('h2').text
    content = article.find('div', class_='content').text

    tokens = word_tokenize(content)
    frequency_distribution = FreqDist(tokens)
    top_words = frequency_distribution.most_common(10)

    print('Title:', title)
    print('Content:', content)
    print('Top Words:', top_words)
    print('---')

Code Analysis: This case demonstrates how to crawl articles from news websites and use natural language processing libraries for text analysis. We send a GET request to get the HTML response of the news article page, and then use the BeautifulSoup library to parse the HTML response. With the find_all method, we find all article elements, which contain news articles. For each article, we use the find method to find the title and content, and print them out. We use the word_tokenize function in the nltk library to tokenize the content and use the FreqDist class to calculate the word frequency distribution. Finally, we print out the top 10 words with the highest frequency.

Case 5: Crawl and analyze stock data

import requests
import pandas as pd

url = 'http://example.com/stock-data'
response = requests.get(url)

data = response.json()

df = pd.DataFrame(data)
df['Date'] = pd.to_datetime(df['Date'])

# 计算股票收益率
df['Return'] = df['Close'].pct_change()

# 计算股票收益率的统计信息
return_stats = df['Return'].describe()

print('Stock Return Statistics:')
print(return_stats)

Code analysis: This case shows how to crawl stock data and use the pandas library for data analysis. We send a GET request to get the JSON response of the stock data, which is then converted into a DataFrame object. We use pd.to_datetime() function to convert date column to datetime format. We then calculate the return on the stock by calculating the percentage change in the daily closing price. Finally, we use the describe() function to calculate stock return statistics and print them out.

Conclusion: In this blog, we introduced five practical Python crawler cases, and provided corresponding code examples and analysis. These cases cover different application scenarios, including crawling weather data, image downloads, movie reviews, news article crawling and text analysis, and stock data crawling and analysis. Through the study of these cases, readers can have a deeper understanding of the application and skills of Python crawlers, and provide more ideas and inspiration for their own crawler projects.

By using Python crawlers, we can obtain data from web pages and perform various processing and analysis. These cases demonstrate the powerful functions of Python crawlers in data acquisition and processing. Readers can further expand and optimize these cases according to their own needs and interests, and apply them to their actual projects.

I hope this blog will help readers understand and apply Python crawler technology, and bring inspiration and motivation to practice. I wish readers to explore more exciting possibilities in the world of reptiles!