ADD file via upload

master
pn2yfabhg 6 months ago
parent 99e88f50c1
commit 5f7d6b8aa7

@ -0,0 +1,110 @@
import requests
from bs4 import BeautifulSoup
import pandas as pd
import random
def requests_get(url):
user_agent_list = [ \
"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1", \
"Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11", \
"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6", \
"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1090.0 Safari/536.6", \
"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/19.77.34.5 Safari/537.1", \
"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.9 Safari/536.5", \
"Mozilla/5.0 (Windows NT 6.0) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.36 Safari/536.5", \
"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", \
"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", \
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", \
"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3"
]
user_agent = random.choice(user_agent_list)
headers = {
"User-Agent": user_agent
}
try:
response = requests.get(url, headers=headers)
response.raise_for_status()
return response.text
except requests.exceptions.RequestException as e:
print(f"请求失败: {e}")
return None
# BeautifulSoup的三个方法find:
# 返回的是第一个符合条件的数据
# 。可以根据title的值来找到对应的标签对象
# ·根据class的值来找需要在class下添加下划线find_a11
# ·返回的是一个列表,并且返回了匹配的所有的标签
# select
# 有了如上基础,对源代码进行标签路径的分析,就可以取值了。
def get_movie_info(url):
soup = BeautifulSoup(requests_get(url), 'html.parser')
item_elements = soup.find_all("div", class_="item")
movie_list = []
for item in item_elements:
movie = {}
# 获取电影排名
movie['ranking'] = item.find('em', class_='').get_text(strip=True)
# 获取电影名称
movie['title'] = item.find('span', class_='title').get_text(strip=True)
# 获取电影详情链接
movie['link'] = item.find('a', href=True)['href']
# 获取电影评分
movie['rating'] = item.find('span', class_='rating_num').get_text(strip=True)
# 获取电影简介
try:
movie['profile'] = item.find('span', class_='inq').get_text(strip=True)
except AttributeError:
movie['profile'] = ''
span_list_len = len(item.find_all('span'))
if span_list_len >= 6:
num_reviews = item.find_all('span')[span_list_len - 2].get_text(strip=True).replace('人评价', '')
movie['num_reviews'] = num_reviews
poster = item.find('img', class_='')['src']
movie['poster'] = poster
movie_infos = item.select('p', class_="")[0].text.strip()
# print(movie_infos)
if movie['title'] == '大闹天宫':
movie['type'] = movie_infos.split('\n')[1].split('/')[4].strip()
movie['country'] = movie_infos.split('\n')[1].split('/')[3].split(' ')[0].strip()
else:
movie['type'] = movie_infos.split('\n')[1].split('/')[2].strip()
movie['country'] = movie_infos.split('\n')[1].split('/')[1].split(' ')[0].strip()
movie['year'] = movie_infos.split('\n')[1].split('/')[0].strip(' ').strip('(中国大陆)')
movie_soup = BeautifulSoup(requests_get(movie['link']), 'html.parser')
movie['director'] = movie_soup.find("a", rel="v:directedBy").get_text()
movie['time'] = movie_soup.find("span", property="v:runtime").get_text(strip=True).replace('片长','')
try:
movie['actor'] = movie_soup.find("a", rel="v:starring").get_text(strip=True)
except AttributeError:
movie['actor'] = ''
movie_list.append(movie)
return movie_list
if __name__ == '__main__':
print(requests_get('https://movie.douban.com/top250'))
base_url = 'https://movie.douban.com/top250'
movie_data = []
for page in range(10):
start = page * 25
# url ='https://movie.douban.com/top250?start=50'
url = f'{base_url}?start={start}'
print(f"开始爬取第 {page + 1} 页: {url}")
movies = get_movie_info(url)
movie_data.extend(movies)
print('爬取完成')
df = pd.DataFrame(movie_data)
# # 将数据保存为CSV文件
csv_file = 'flaskProject/data/export.csv'
df.to_csv(csv_file, index=False, encoding='utf-8')
print('存储完成')
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