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

python 复制代码
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'<span>(\d*)人评价</span>'), 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

python 复制代码
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

python 复制代码
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

本人读研期间发表5篇SCI数据挖掘相关论文,现在某研究院从事数据算法相关科研工作,对Python有一定认知和理解,会结合自身科研实践经历不定期分享关于python、机器学习、深度学习等基础知识与应用案例。

致力于只做原创,以最简单的方式理解和学习,关注我一起交流成长。

1、邀请三个朋友关注本订阅号或2、分享/在看任意订阅号的三篇文章即可在后台联系我获取相关数据集和源码。

2、关注"数据杂坛"公众号,点击"领资料"即可免费领取资料书籍。

3、如果对本文有疑问,或者有论文指导的相关需求,点击"联系我"添加作者微信直接交流。

相关推荐
放飞自我的Coder24 分钟前
【python ROUGE BLEU jiaba.cut NLP常用的指标计算】
python·自然语言处理·bleu·rouge·jieba分词
正义的彬彬侠1 小时前
【scikit-learn 1.2版本后】sklearn.datasets中load_boston报错 使用 fetch_openml 函数来加载波士顿房价
python·机器学习·sklearn
张小生1801 小时前
PyCharm中 argparse 库 的使用方法
python·pycharm
秃头佛爷1 小时前
Python使用PDF相关组件案例详解
python
Dxy12393102161 小时前
python下载pdf
数据库·python·pdf
叶知安1 小时前
如何用pycharm连接sagemath?
ide·python·pycharm
weixin_432702261 小时前
代码随想录算法训练营第五十五天|图论理论基础
数据结构·python·算法·深度优先·图论
菜鸟清风1 小时前
ChromeDriver下载地址
python
deephub1 小时前
Tokenformer:基于参数标记化的高效可扩展Transformer架构
人工智能·python·深度学习·架构·transformer
xiaoxiongip6662 小时前
HTTP 和 HTTPS
网络·爬虫·网络协议·tcp/ip·http·https·ip