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• 【python014】Python爬取并解析潮汐天气简报-潮历数据，源代码下载
• 【python014】Python爬取并解析潮汐天气简报-潮历数据，源代码下载
• 【python014】Python爬取并解析潮汐天气简报-潮历数据，源代码下载

1.简要介绍

• 参考潮汐数据，网站地址：潮汐表 快速导航，做进一步层次应用。数据可视化效果如下图所示：
  
• 发现大语言模型写的并不能解决问题，也可能是没付费的原因，haha
• 趁着空闲的时间撸了段代码，供已参考

2.Python版数据爬取、解析代码

• 源代码

  import re
  import requests
  import pandas as pd
  from datetime import datetime
  from bs4 import BeautifulSoup 
  import warnings
  warnings.filterwarnings('ignore')
  
  pd.set_option('display.width', 500)
  pd.set_option('display.max_rows', 200)
  pd.set_option('display.max_columns', 200)
  pd.set_option('display.max_colwidth', 1000)
  
  # 浙江-宁波市-松兰山
  zj_nb_sl_url = 'https://www.eisk.cn/Calendar/1259.html'
  zj_nb_sl_name = '浙江-宁波市-松兰山'
  # 浙江-象山县-西泽
  zj_xs_xz_url = 'https://www.eisk.cn/Calendar/463.html'
  zj_xs_xz_name = '浙江-象山县-西泽'
  # 浙江-象山县-石浦港
  zj_xs_sp_url = 'https://www.eisk.cn/Calendar/460.html'
  zj_xs_sp_name = '浙江-象山县-石浦港'
  
  wl15tcxb = '未来15天潮汐表'
  
  def parser_response_html( _url, _name ):
      response = requests.get( _url )  # 发送HTTP请求  
      response.raise_for_status()  # 如果请求失败，则抛出HTTPError异常  
  
      # 解析HTML  
      soup = BeautifulSoup(response.text, 'html.parser')
      og_title_desc = soup.find(attrs={"property": "og:description"})['content']
  
      result_html = []
      for a_slice in soup.find_all('a', href = re.compile(r'/Tides/\d+\.html\?date=.*?')):
          hour = a_slice.find('div', class_='hour').text.strip()
          hour_date = parser_ymd_date( hour )
  
          day = a_slice.find('div', class_='day').text.strip()
          _temperature = a_slice.find('div', class_='temperature').text.strip()
          humidity = a_slice.find('div', class_='humidity').text.strip()
  
          temperature = a_slice.find('div', class_ = 'temperature', style = re.compile(r'color:.*?') ).text.strip()
  
          tide2 = ';'.join( [','.join( c.string.strip() for c in t.contents ) for t in a_slice.find_all('div', attrs={'class':'tide2'})] )
  
          skycon = a_slice.find('div', class_='skycon').text.strip()
          visibility = a_slice.find('div', class_='visibility').text.strip()
          dswrf = a_slice.find('div', class_='dswrf').text.strip()
  
          wave2 = a_slice.find('div', class_='wave2').text.strip()
          wave1 = a_slice.find('div', class_='wave1').text.strip()
          wave3 = ''
          if a_slice.find('div', class_='wave3'):
              wave3 = a_slice.find('div', class_='wave3').text.strip()
  
          wind2 = a_slice.find('div', class_='wind2').text.strip()
          wind1 = a_slice.find('div', class_='wind1').text.strip()
          wind3 = a_slice.find('div', class_='wind3').text.strip()
  
          description = ''
          for desc in a_slice.find_all('div', class_='description'):
              description += desc.text.strip().replace('\n', '').replace('\r', '').replace(' ', '')
          result_html.append( [og_title_desc, hour_date, day, _temperature, humidity, temperature, tide2, skycon, visibility, dswrf, wave2, wave1, wave3, wind2, wind1, wind3 , description] )
      result_df = pd.DataFrame( result_html )
      return result_df
  
  zj_nb_sl_df = parser_response_html( zj_nb_sl_url, zj_nb_sl_name )
  zj_xs_xz_df = parser_response_html( zj_xs_xz_url, zj_xs_xz_name )
  zj_xs_sp_df = parser_response_html( zj_xs_sp_url, zj_xs_sp_name )
  
  union_df = pd.concat( [zj_nb_sl_df, zj_xs_xz_df, zj_xs_sp_df] )
  
  union_df.columns = [ 'title' ,'hour_date' ,'day' ,'lunar_calendar' ,
                       'humidity' ,'temperature' ,'tide2' ,'skycon' ,'visibility' ,'dswrf' ,'wave2' ,'wave1' ,
                       'wave3' ,'wind2' ,'wind1' ,'wind3 ' ,'description']
  
  union_df.to_excel('./%s-%s-%s-%s.xlsx' % ( zj_nb_sl_name ,zj_xs_xz_name ,zj_xs_sp_name ,wl15tcxb ), index=None, encoding='utf8')
  

• 数据样例解析效果如下图所示：
  

3.参考地址

• 潮汐表 快速导航
• 【python014】Python爬取并解析潮汐天气简报-潮历数据源代码下载