Python数据分析实战-爬取DouBan电影前250的相关信息并写入Excel表中（附源码和实现效果）
实现功能
在win10操作系统环境下，基于python3.10解释器，爬取豆瓣电影Top250的相关信息并将爬取的信息写入Excel表中。
实现代码
采集爬取模块：scraper.py
import requests
from bs4 import BeautifulSoup
from typing import List
import re
class Movie:
def __init__(self, detail_link: str, image_link: str, chinese_name: str, foreign_name: str, rating: float, review_count: int, overview: str, director: str, actors: str, year: int, region: str, category: str):
self.detail_link = detail_link
self.image_link = image_link
self.chinese_name = chinese_name
self.foreign_name = foreign_name
self.rating = rating
self.review_count = review_count
self.overview = overview
self.director = director
self.actors = actors
self.year = year
self.region = region
self.category = category
class Scraper:
def __init__(self, base_url: str):
self.base_url = base_url
self.movies = []
def scrape(self) -> List[Movie]:
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0",
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
"Cookie": "bid=m9sDMeuTWp4; ap_v=0,6.0; _pk_id.100001.4cf6=d6615bd2530852c6.1700447648.; _pk_ses.100001.4cf6=1; __utma=30149280.633232779.1700447649.1700447649.1700447649.1; __utmb=30149280.0.10.1700447649; __utmc=30149280; __utmz=30149280.1700447649.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); __utma=223695111.1435231277.1700447649.1700447649.1700447649.1; __utmb=223695111.0.10.1700447649; __utmc=223695111; __utmz=223695111.1700447649.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); _cc_id=748927837a892b664c1f1ab42fbe510a; panoramaId_expiry=1700534054317; panoramaId=18a92c0e9b136f927d0f0871ae33a9fb927a9d987bb8aa39557c58077684bc2c; panoramaIdType=panoDevice; _pbjs_userid_consent_data=3524755945110770; __gads=ID=7617c807b66fd695:T=1700447653:RT=1700448285:S=ALNI_MY0jxMNVX0GooLXe8dtdh74vfdLvQ; __gpi=UID=00000cdbaaf33934:T=1700447653:RT=1700448285:S=ALNI_MYekZkuVr46VHfZjhuhdX2kpLxOkw; cto_bundle=xIP-n181MjZFSVBGdlMlMkJEY3hvY3dycER1QjhISjdGU2dzOWxWZUFSMmNZd25VQ1Y0REdtaXZPdTh2aEJGUCUyQlo3WjVETzVNc2VUSFR3dHFXQVRRZU1ZejdOMXk5RDM4VjV1WkJsRWVXd1dQdjRvRE1JQjhEVkJQUVEyV0M1dlgzVkFBclZDTnJWM1g3MWZERDltRFR1UDZZNXp3JTNEJTNE; cto_bidid=vr7nBV8lMkZGJTJCOGVQWjhWREJUelpJYm1UdFBWaWd5bk9WT1JCdyUyRjlpN1duSWFZd3JPR2dkdmh1Q2tNa3NJa25rQTExSFlPM1p2YzdpT1U2cDE5UUowU3p1VHk3YkhVWWw4aFBmUExiZmtZdWtPS3U4byUzRA; cto_dna_bundle=14GGU181MjZFSVBGdlMlMkJEY3hvY3dycER1QiUyQmxhTVFwSEdNWHZ6OE5MZ2olMkJQbjlyODR2SWtIJTJCUGZmYm40Z3p5b1AxbSUyRkJKVDBVUVlXbGE1ZWRQeVUlMkJmeTR5dyUzRCUzRA",
}
for i in range(0, 10): # 左闭右开
self.url = self.base_url + str(i * 25) # 字符串的拼接，调用获取页面信息的函数，10次（一共10页)
response = requests.get(self.url, headers=headers)
soup = BeautifulSoup(response.text, 'html.parser')
movie_elements = soup.find_all('div', class_='item')
for movie_element in movie_elements:
detail_link = movie_element.find('a')['href']
image_link = movie_element.find('img')['src']
title_element = movie_element.find('div', class_='hd')
chinese_name = title_element.find('span', class_='title').text
foreign_name = title_element.find('span', class_='other').text.strip()[2:]
rating = float(movie_element.find('span', class_='rating_num').text)
# review_count = int(movie_element.find('span', class_='rating_people').find('span').text)
review_count = re.findall(re.compile(r'(\d*)人评价'), str(movie_element))[0]
overview = movie_element.find('span', class_='inq').text if movie_element.find('span', class_='inq') else ''
info_text = movie_element.find('div', class_='bd').find('p').text
director = info_text.split('导演: ')[1].split(' ')[0]
actors = info_text.split('主演: ')[1].split(' ')[0] if '主演: ' in info_text else ''
year_region_category = info_text.split('\n')[-2].strip().split('/')
try:
year = int(year_region_category[0].strip())
except ValueError as e:
print(e)
year = None
region = year_region_category[-2].strip()
category = year_region_category[-1].strip()
movie = Movie(detail_link, image_link, chinese_name, foreign_name, rating, review_count, overview, director, actors, year, region, category)
self.movies.append(movie)
return self.movies写入文件模块：writer.py
import pandas as pd
from typing import List
from openpyxl import Workbook
from openpyxl.utils.dataframe import dataframe_to_rows
from scraper import Movie # Import the Movie class
class Writer:
def __init__(self, file_path: str):
self.file_path = file_path
def write(self, movies: List[Movie]): # Specify the type of objects in the list
data = {
'Detail Link': [movie.detail_link for movie in movies],
'Image Link': [movie.image_link for movie in movies],
'Chinese Name': [movie.chinese_name for movie in movies],
'Foreign Name': [movie.foreign_name for movie in movies],
'Rating': [movie.rating for movie in movies],
'Review Count': [movie.review_count for movie in movies],
'Overview': [movie.overview for movie in movies],
'Director': [movie.director for movie in movies],
'Actors': [movie.actors for movie in movies],
'Year': [movie.year for movie in movies],
'Region': [movie.region for movie in movies],
'Category': [movie.category for movie in movies]
}
df = pd.DataFrame(data)
wb = Workbook()
ws = wb.active
for r in dataframe_to_rows(df, index=False, header=True):
ws.append(r)
wb.save(self.file_path)主程序模块：main.py
from scraper import Scraper, Movie
from writer import Writer
def main():
# base_url = 'https://movie.douban.com/top250'
base_url = "https://movie.douban.com/top250?start="
file_path = 'douban_movies.xlsx'
# Initialize scraper and scrape data
scraper = Scraper(base_url)
movies = scraper.scrape()
# Initialize writer and write data to file
writer = Writer(file_path)
writer.write(movies)
if __name__ == '__main__':
main()实现效果
写在后面
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