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如何用python爬取微博热搜数据并保存

来源:互联网 作者:佚名 时间:2021-09-10 02:17
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 bs4mylist=[]r = requests.get(url='https://s.weibo.com/top/summaryRefer=top_hottopnav=1wvr=6',timeout=10)print(r.status_code) # 获取返回状态r.encoding=

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]))

效果

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')

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