网站建设的页面要求网络营销做得比较好的企业

在当今数据驱动的时代，获取和分析网络数据已成为许多领域的重要技能。本文将详细介绍如何使用Python爬取豆瓣电影Top250榜单数据，并将结果保存到Excel文件中。这个项目不仅适合Python初学者学习网络爬虫基础，也能帮助数据分析师获取有价值的电影数据。

一、项目概述

豆瓣电影Top250是一个非常有价值的电影榜单，包含了全球范围内评价最高的250部电影。通过爬取这些数据，我们可以进行各种有趣的分析，比如：

• 哪些导演的作品上榜最多？

• 哪些年份的电影质量最高？

• 不同类型电影的评分分布如何？

二、准备工作

在开始之前，我们需要安装必要的Python库：

pip install requests beautifulsoup4 openpyxl

这些库的作用分别是：

• requests：发送HTTP请求获取网页内容

• beautifulsoup4：解析HTML文档

• openpyxl：操作Excel文件

三、代码解析

1. 导入所需库

import re
import requests
from bs4 import BeautifulSoup
import openpyxl
from openpyxl import Workbook

2. 创建Excel工作簿

# 创建excel工作表
wb = Workbook()
ws = wb.active
ws.title = "豆瓣电影top250"# 写入表头
ws.append(["排名", "电影名称", "英文名称", "其他名称", "导演", "主演", "年份", "国家", "类型", "评分", "评价人数", "简介"])

3. 设置请求头(headers)和cookies

headers = {"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/135.0.0.0 Safari/537.36",# 其他headers...
}cookies = {"bid": "DwhFQXCqdEQ",# 其他cookies...
}

4. 主爬取逻辑

for i in range(0, 250, 25):# 确定网址url = f"https://movie.douban.com/top250?start={i}&filter="# 发起请求response = requests.get(url, headers=headers, cookies=cookies)soup = BeautifulSoup(response.text, "html.parser")for item in soup.find_all("div", class_="item"):# 解析各个数据字段...

5. 数据解析详解

获取电影排名

rank = item.find("em").text

获取电影名称

title = item.find("span", class_="title").text

获取导演和主演信息

info = item.find("p").get_text()
info_lines = [line.strip() for line in info.split("\n") if line.strip()]director = ""
actors = ""
if info_lines:director_part = info_lines[0].replace("导演:", "").strip()director_parts = director_part.split("主演:")director = director_parts[0].strip()if len(director_parts) > 1:actors = director_parts[1].strip()

获取评分和评价人数

rating_num = item.find("span", class_="rating_num")
rating = rating_num.text if rating_num else ""votes_span = item.find("span", string = lambda text: text and "人评价" in text)
votes = votes_span.text.replace("人评价", "") if votes_span else ""

6. 保存数据到Excel

wb.save("豆瓣电影top250.xlsx")
print("数据已经保存到豆瓣电影top250.xlsx")

四、完整代码

import re
import requests
from bs4 import BeautifulSoup
import openpyxl
from openpyxl import Workbook# 创建excel工作表
wb = Workbook()
ws = wb.active
ws.title = "豆瓣电影top250"# 写入表头
ws.append(["排名", "电影名称", "英文名称", "其他名称", "导演", "主演", "年份", "国家", "类型", "评分", "评价人数", "简介"])headers = {"accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7","accept-language": "zh-CN,zh;q=0.9","cache-control": "no-cache","pragma": "no-cache","priority": "u=0, i","referer": "https://movie.douban.com/top250","sec-ch-ua": "\"Google Chrome\";v=\"135\", \"Not-A.Brand\";v=\"8\", \"Chromium\";v=\"135\"","sec-ch-ua-mobile": "?0","sec-ch-ua-platform": "\"Windows\"","sec-fetch-dest": "document","sec-fetch-mode": "navigate","sec-fetch-site": "same-origin","sec-fetch-user": "?1","upgrade-insecure-requests": "1","user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/135.0.0.0 Safari/537.36"
}
cookies = {"bid": "DwhFQXCqdEQ","_pk_id.100001.4cf6": "7f5a4ed7ee20a463.1744699317.","ap_v": "0,6.0","__utmc": "223695111","__utmz": "223695111.1744699317.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none)","__yadk_uid": "jb67OdmeCnxkr8xj7kmcnNIblmohGNR6","_pk_ses.100001.4cf6": "1","__utma": "223695111.257683867.1744699317.1744699317.1744703580.2","__utmb": "30149280.8.10.1744703580","dbcl2": "\"288340287:KHPK9ybQcc8\"","ck": "NPUI","push_noty_num": "0","push_doumail_num": "0","__utmt": "1","__utmv": "30149280.28834"
}for i in range(0, 250, 25):# 确定网址url = f"https://movie.douban.com/top250?start={i}&filter="# 发起请求response = requests.get(url, headers=headers, cookies=cookies)soup = BeautifulSoup(response.text, "html.parser")for item in soup.find_all("div", class_="item"):# 排名rank = item.find("em").text# 电影名称title = item.find("span", class_="title").text# 英文名称english_title = item.find("span", class_="title").find_next_sibling("span").text# 其他名称other_names = item.find("span", class_="other").text# 导演和主演信息bd = item.find("div", class_="bd")info = item.find("p").get_text()info_lines = [line.strip() for line in info.split("\n") if line.strip()]# 处理导演和主演director = ""actors = ""if info_lines:director_part = info_lines[0].replace("导演:", "").strip()director_parts = director_part.split("主演:")director = director_parts[0].strip()if len(director_parts) > 1:actors = director_parts[1].strip()# 处理年份、国家、类型year = country = genre = ""if len(info_lines) > 1:line2 = info_lines[1]parts = re.split(r'\s*/\s*', line2.strip())if parts:year = parts[0] if len(parts) > 0 else ""country = parts[1] if len(parts) > 1 else ""genre = parts[2] if len(parts) > 2 else ""# 评分rating_num = item.find("span", class_="rating_num")rating = rating_num.text if rating_num else ""# 评价人数votes_span = item.find("span", string = lambda text: text and "人评价" in text)votes = votes_span.text.replace("人评价", "") if votes_span else ""# 简介quote_span = item.find("p", class_="quote")quote = quote_span.find("span").text if quote_span and quote_span.find("span") else ""# 写入excelws.append([rank, title, english_title, other_names, director, actors, year, country, genre, rating, votes, quote])# 保存excel文件
wb.save("豆瓣电影top250.xlsx")
print("数据已经保存到豆瓣电影top250.xlsx")

五、可能遇到的问题及解决方案

1. 反爬虫机制：

   • 如果被封IP，可以尝试使用代理IP

   • 增加请求间隔时间：time.sleep(random.uniform(1, 3))

2. 页面结构变化：

   • 定期检查CSS选择器是否仍然有效

   • 使用更灵活的解析方式，如正则表达式

3. 数据缺失处理：

   • 添加异常处理，确保某个字段缺失时程序不会中断

六、进一步改进建议

1. 添加异常处理：

   try:response = requests.get(url, headers=headers, cookies=cookies)response.raise_for_status()
   except requests.exceptions.RequestException as e:print(f"请求失败: {e}")continue

2. 增加数据清洗功能：

   def clean_text(text):return text.strip().replace("\n", "").replace("\r", "")

3. 添加进度显示：

   print(f"正在处理第{i//25 + 1}页，共10页...")

4. 支持命令行参数：

   import argparse
   parser = argparse.ArgumentParser()
   parser.add_argument("--output", default="豆瓣电影top250.xlsx")
   args = parser.parse_args()
   wb.save(args.output)

七、结语

通过这个项目，我们学习了如何使用Python爬取网页数据并保存到Excel。这个技能可以应用于许多其他场景，如爬取商品信息、新闻数据等。记住要遵守网站的robots.txt规定，合理控制爬取频率，不要给目标网站造成过大负担。