用python爬取豆瓣评分数据(附完整源码）爬豆瓣电影上指定电影的短评（保姆级别教学）

本文链接：https://blog.youkuaiyun.com/2301_79629586/article/details/135294561

话不多说，先给大家上我亲测的完整代码

# 程序功能：爬豆瓣电影上指定电影的短评

import pymysql
import requests
from bs4 import BeautifulSoup
import pandas as pd
import os
import random
from time import sleep

# 请求头
h1 = {
    'Cookie': 'bid=urNzWwdwG5s; gr_user_id=7b083a3b-f999-45f3-8b57-899422537462; '
              '__gads=ID=c43e6cbe5e55b07b-220c4766e6db0021:T=1678071910:RT=1678071910:S'
              '=ALNI_MYqyEHZciozJa5VoMA2XjC6Iu7TQw; '
              '__gpi=UID=00000bd25a25a974:T=1678071910:RT=1678071910:S=ALNI_Mb1QiGjI2Rl9LZ8AeROjj3zTZ6cRw; '
              'll="108309"; _pk_id.100001.4cf6=aeb26857e6a2d511.1703750925.; '
              '_vwo_uuid_v2=DE46F19B4614438DB1ADF3E25DF9BDCE8|47992c4b59d5dd1e2ff819eac089c1fc; '
              '__yadk_uid=oTFPD3WaMBoHvQiCXegnItFup6Z6o7dH; '
              '_pk_ref.100001.4cf6=%5B%22%22%2C%22%22%2C1703817890%2C%22https%3A%2F%2Fcn.bing.com%2F%22%5D; ap_v=0,'
              '6.0; __utma=30149280.1505852171.1678071877.1703750925.1703817891.3; __utmc=30149280; '
              '__utmz=30149280.1703817891.3.2.utmcsr=cn.bing.com|utmccn=(referral)|utmcmd=referral|utmcct=/; '
              '__utma=223695111.589624156.1703750925.1703750925.1703817891.2; __utmc=223695111; '
              '__utmz=223695111.1703817891.2.2.utmcsr=cn.bing.com|utmccn=(referral)|utmcmd=referral|utmcct=/',
    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
    'Accept-Encoding': 'gzip, deflate',
    'Host': 'movie.douban.com',
    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) '
                  'Version/15.4 Safari/605.1.15',
    'Accept-Language': 'zh-CN,zh-Hans;q=0.9',
    'Referer': 'https://movie.douban.com/subject/35267224/?from=showing',
    'Connection': 'keep-alive'
}


def trans_star(v_str):
    """转换评论星级"""
    v_str = v_str[0]
    if v_str == 'allstar10':
        return '1星'
    elif v_str == 'allstar20':
        return '2星'
    elif v_str == 'allstar30':
        return '3星'
    elif v_str == 'allstar40':
        return '4星'
    elif v_str == 'allstar50':
        return '5星'
    else:
        return '未知'


def get_short(v_movie_id):
    """爬取短评数据"""
    global dt
    for page in range(1, max_page + 1):  # 爬取前n页
        requests.packages.urllib3.disable_warnings()
        # 请求地址
        url = 'https://movie.douban.com/subject/{}/comments?start={}&limit=20&status=P&sort=new_score'.format(
            v_movie_id, (page - 1) * 20)
        # 发送请求
        response = requests.get(url, headers=h1, verify=False)
        print(response.status_code)
        # 解析页面数据
        soup = BeautifulSoup(response.text, 'html.parser')
        # 所有评论数据
        reviews = soup.find_all('div', {'class': 'comment'})
        print('开始爬取第{}页，共{}条评论'.format(page, len(reviews)))
        sleep(random.uniform(1, 2))
        # 定义空列表用于存放数据
        user_name_list = []  # 评论者昵称
        star_list = []  # 评论星级
        time_list = []  # 评论时间
        ip_list = []  # 评论者ip属地
        vote_list = []  # 有用数
        content_list = []  # 评论内容
        for review in reviews:
            # 评论者昵称
            user_name = review.find('span', {'class': 'comment-info'}).find('a').text
            user_name_list.append(user_name)
            # 评论星级
            star = review.find('span', {'class': 'comment-info'}).find_all('span')[1].get('class')
            star = trans_star(star)
            star_list.append(star)
            # 评论时间
            time2 = review.find('span', {'class': 'comment-time'}).text.strip()
            print('评论时间：', time2)
            time_list.append(time2)
            # 评论者IP属地
            ip = review.find('span', {'class': 'comment-location'}).text
            ip_list.append(ip)
            # 有用数
            vote = review.find('span', {'class': 'votes vote-count'}).text
            vote_list.append(vote)
            # 评论内容
            content = review.find('span', {'class': 'short'}).text
            content = content.replace(',', '，').replace(' ', '').replace('\n', '').replace('\t', '').replace('\r', '')
            content_list.append(content)
        df = pd.DataFrame(
            {
                '页码': page,
                '评论者昵称': user_name_list,
                '评论星级': star_list,
                '评论时间': time_list,
                '评论者IP属地': ip_list,
                '有用数': vote_list,
                '评论内容': content_list,
            }
        )
        data_json = df.to_dict(orient='records')

        for dt in data_json:
            print(dt)
        sql = 'insert into douban_data values ("%s","%s","%s","%s","%s","%s","%s")' % (
            dt['页码'], dt['评论者昵称'], dt['评论星级'], dt['评论时间'], dt['评论者IP属地'], dt['有用数'],
            dt['评论内容'])
        cursor.execute(sql)  # 执行语句
        db.commit()
        # 定义sql语句

        cursor.execute(sql)  # 执行语句
        db.commit()
        # 设置csv文件表头
        if os.path.exists(result_file):
            header = False
        else:
            header = True
        # 保存到csv文件
        df.to_csv(result_file, mode='a+', header=header, index=False, encoding='utf_8_sig')
        print('文件保存成功：', result_file)


if __name__ == '__main__':
    # host:地址    potr:端口     user:用户名    password:密码     db:数据库名     charset:编码
    db = pymysql.connect(host="localhost", port=3306, user="root", password="ping1212", db="豆瓣", charset="utf8mb4")
    cursor = db.cursor()  # 创建游标

    # 电影id
    movie_id = '35556001'
    # 最大爬取页
    max_page = 5  # 最大为30页
    # 保存文件名
    result_file = '豆瓣短评_{}_前{}页.csv'.format(movie_id, max_page)
    # 如果csv文件存在，先删除之
    if os.path.exists(result_file):
        os.remove(result_file)
        print('结果文件存在，已删除: {}'.format(result_file))
    # 循环爬取短评
    get_short(movie_id)  # 定义数据库

要想代码运行成功，还需要做以下准备工作：