数据结构化与保存

1.结构化：

• 单条新闻的详情字典：news
• 一个列表页所有单条新闻汇总列表：newsls.append(news)
• 所有列表页的所有新闻汇总列表：newstotal.extend(newsls)

2.转换成pandas的数据结构DataFrame

3.从DataFrame保存到excel

4.从DataFrame保存到sqlite3数据库

 

import requests
from bs4 import BeautifulSoup
from datetime import datetime
import re
import pandas
import sqlite3
    

def getclick(url):
    m = re.search(r'_(.*).html',url)
    newsid=m.group(1)[5:]
    clickurl = 'http://oa.gzcc.cn/api.php?op=count&id={}&modelid=80'.format(newsid)
    resc= requests.get(clickurl).text
    r =re.search(r'hits(.*)',resc).group(1)
    click=r.lstrip("').html('").rstrip("');")
    return(int(click))

def getdetail(url):
    resd=requests.get(url)
    resd.encoding='utf-8'   
    soupd=BeautifulSoup(resd.text,'html.parser')
    news={}
    news['url']=url
    news['title']=soupd.select('.show-title')[0].text
    info=soupd.select('.show-info')[0].text
    news['dt']=datetime.strptime(info.lstrip('发布时间:')[0:19],'%Y-%m-%d %H:%M:%S')
    news['source']=re.search('来源：(.*)点击',info).group(1).strip()
    #news['countent']=soupd.select('.show-content')[0].text.strip()
    news['click']=getclick(url)
    return(news)

def onepage(pageurl):
    res = requests.get(pageurl)
    res.encoding='utf-8'   
    soup=BeautifulSoup(res.text,'html.parser')
    newsls = []
    for news in soup.select('li'):
        if len(news.select('.news-list-title'))>0:
            newsls.append(getdetail(news.select('a')[0]['href']))
    return(newsls)

#print(onepage('http://news.gzcc.cn/html/xiaoyuanxinwen/'))   


newstotal = []
gzccurl='http://news.gzcc.cn/html/xiaoyuanxinwen/'
newstotal.extend(onepage(gzccurl))

res = requests.get(gzccurl)
res.encoding= 'utf-8'
soup=BeautifulSoup(res.text,'html.parser')
n = int(soup.select('.a1')[0].text.rstrip('条'))
pages=n//10+1

for i in range(2,3):
    listurl='http://news.gzcc.cn/html/xiaoyuanxinwen/{}.html'.format(i)
    newstotal.extend(onepage(listurl))

df = pandas.DataFrame(newstotal)
#print(df.head())

df.to_excel('gzccnews.xlsx')

with sqlite3.connect('gzccnewsdb.sqlite') as db:
    df.to_sql('gzccnewsdb8',con = db)