如何用python爬取微博热搜数据并保存

主要用到requests和bf4两个库
将获得的信息保存在d://hotsearch.txt下

import requests;
import bs4
mylist=[]
r = requests.get(url='https://s.weibo.com/top/summary?Refer=top_hot&topnav=1&wvr=6',timeout=10)
print(r.status_code) # 获取返回状态
r.encoding=r.apparent_encoding
demo = r.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(demo,"html.parser")
for link in soup.find('tbody') :
hotnumber=''
if isinstance(link,bs4.element.Tag):
#  print(link('td'))
 lis=link('td')
 hotrank=lis[1]('a')[0].string#热搜排名
 hotname=lis[1].find('span')#热搜名称
 if isinstance(hotname,bs4.element.Tag):
  hotnumber=hotname.string#热搜指数
  pass
 mylist.append([lis[0].string,hotrank,hotnumber,lis[2].string])
f=open("d://hotsearch.txt","w+")
for line in mylist:
f.write('％s ％s ％s ％s\n'％(line[0],line[1],line[2],line[3]))

知识点扩展：利用python爬取微博热搜并进行数据分析

爬取微博热搜

import schedule
import pandas as pd
from datetime import datetime
import requests
from bs4 import BeautifulSoup

url = "https://s.weibo.com/top/summary?cate=realtimehot&sudaref=s.weibo.com&display=0&retcode=6102"
get_info_dict = {}
count = 0

def main():
 global url, get_info_dict, count
 get_info_list = []
 print("正在爬取数据~~~")
 html = requests.get(url).text
 soup = BeautifulSoup(html, 'lxml')
 for tr in soup.find_all(name='tr', class_=''):
   get_info = get_info_dict.copy()
   get_info['title'] = tr.find(class_='td-02').find(name='a').text
   try:
     get_info['num'] = eval(tr.find(class_='td-02').find(name='span').text)
   except AttributeError:
     get_info['num'] = None
   get_info['time'] = datetime.now().strftime("％Y/％m/％d ％H:％M")
   get_info_list.append(get_info)
 get_info_list = get_info_list[1:16]
 df = pd.DataFrame(get_info_list)
 if count == 0:
   df.to_csv('datas.csv', mode='a+', index=False, encoding='gbk')
   count += 1
 else:
   df.to_csv('datas.csv', mode='a+', index=False, header=False, encoding='gbk')

# 定时爬虫
schedule.every(1).minutes.do(main)

while True:
 schedule.run_pending()

pyecharts数据分析

import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Bar, Timeline, Grid
from pyecharts.globals import ThemeType, CurrentConfig

df = pd.read_csv('datas.csv', encoding='gbk')
print(df)
t = Timeline(init_opts=opts.InitOpts(theme=ThemeType.MACARONS)) # 定制主题
for i in range(int(df.shape[0]/15)):
 bar = (
   Bar()
     .add_xaxis(list(df['title'][i*15: i*15+15][::-1])) # x轴数据
     .add_yaxis('num', list(df['num'][i*15: i*15+15][::-1])) # y轴数据
     .reversal_axis() # 翻转
     .set_global_opts( # 全局配置项
     title_opts=opts.TitleOpts( # 标题配置项
       title=f"{list(df['time'])[i * 15]}",
       pos_right="5％", pos_bottom="15％",
       title_textstyle_opts=opts.TextStyleOpts(
         font_family='KaiTi', font_size=24, color='#FF1493'
       )
     ),
     xaxis_opts=opts.AxisOpts( # x轴配置项
       splitline_opts=opts.SplitLineOpts(is_show=True),
     ),
     yaxis_opts=opts.AxisOpts( # y轴配置项
       splitline_opts=opts.SplitLineOpts(is_show=True),
       axislabel_opts=opts.LabelOpts(color='#DC143C')
     )
   )
     .set_series_opts( # 系列配置项
     label_opts=opts.LabelOpts( # 标签配置
       position="right", color='#9400D3')
   )
 )
 grid = (
   Grid()
     .add(bar, grid_opts=opts.GridOpts(pos_left="24％"))
 )
 t.add(grid, "")
 t.add_schema(
   play_interval=1000, # 轮播速度
   is_timeline_show=False, # 是否显示 timeline 组件
   is_auto_play=True, # 是否自动播放
 )

t.render('时间轮播图.html')

来源：https://blog.csdn.net/naiue/article/details/106876989