基于机器学习的电影票房预测平台.zip

# -*- coding: utf-8 -*- """ Created on Sat Dec 7 09:57:06 2019 @author: chensiyi """ import os import requests from lxml import etree import pandas as pd headers = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.84 Safari/537.36'} cookies = {'SUB': 'SECKEY_ABVK=G0NOubs+G1nd4GaVk1B3pk/C1l39FynwIcmNyMVK9hI%3D; ' 'BMAP_SECKEY=Qk_XaCnqY4Pk3xPt73kG36fnqwwTbqw39FxSp9pzdMPMRSH9DCRFzQ' 'eaxsFKMO2X0TKd17xnVOjoUZSQja_S5pYgupzA-19Qg5mC_RHcy9ilaTQ3SkCZJrwOyxC0L' '46Y-3wVPAZUvpzGzeC4A6uaNlu5XVZLRCGkG1wQlIoCEOfQTMp3KLTSq8fnIGlRUys; route=65389440' 'feb63b53ee0576493abca26d; Hm_lvt_82932fc4fc199c08b9a83c4c9d02af11=1649211467,1649236158;' ' Hm_lpvt_82932fc4fc199c08b9a83c4c9d02af11=1649236158; SECKEY_ABVK=G0NOubs+G1nd4GaVk1B3pgEpF' 'CxBxNYOClDwCeOq5T8%3D; BMAP_SECKEY=Qk_XaCnqY4Pk3xPt73kG36pvEjWNUGgz5A1jAJXlfKXuzooPaG-0nHvFrlM' 'w78voHLv0IqFeLpUcmSb6ukszfy3ggQeq0jiqZNZhIXzYMbx6kdku1C2o0-g6jqt6Ry6tEe6h8L8yf01uaXLT10Oz2UQNkQ_' 'vVGaQXdz8MH4ubek2alqG6ouxTu1Z8hprY6YB'} def get_data(url): r = requests.get(url, headers=headers, cookies=cookies, timeout=30) r.raise_for_status() # 查看是否正常，正常返回200,否则返回404等 r.encoding = 'utf-8' return r.text local_data = 'D:/D_showlidaosee' local_main2 = local_data + '/' + 'movie.csv' # 设置路径 if not os.path.exists(local_main2): data = pd.DataFrame(columns=['电影名称', '电影详情页', '电影类型', '电影票房', '国家及地区', '上映时间']) data.to_csv(local_main2, index=None, encoding="utf_8_sig") urls = [] for year in range(5): urls.append('https://ys.endata.cn/BoxOffice/Ranking') for url in urls: data = get_data(url) selector = etree.HTML(data) url = selector.xpath('//td[@class="one"]/a/@href') # 详情页url name = selector.xpath('//td[@class="one"]/a/@title') # 电影名称 movie_type = selector.xpath('//*[@id="tbContent"]//tr//td[2]/text()') # 电影类型 box_office = selector.xpath('//*[@id ="tbContent"]//tr//td[3]/text()') # 电影票房 country = selector.xpath('//*[@id="tbContent"]//tr//td[6]/text()') # 国家及地区 time = selector.xpath('//*[@id="tbContent"]//tr//td[7]/text()') # 上映时间 for i in range(len(url)): data = pd.DataFrame({'电影名称': name[i], '电影详情页': url[i], '电影类型': movie_type[i], '电影票房': box_office[i], '国家及地区': country[i], '上映时间': time[i]}, columns=['电影名称', '电影详情页', '电影类型', '电影票房', '国家及地区', '上映时间'], index=[0]) data.to_csv(local_main2, index=None, mode='a', header=None, sep=',', encoding="utf_8_sig")