数据结构化与保存

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

news_list = []


def crawlOnePageSchoolNews(page_url):
    res0 = requests.get(page_url)
    res0.encoding = ‘UTF-8‘
    soup0 = BeautifulSoup(res0.text, ‘html.parser‘)
    news = soup0.select(‘.news-list > li‘)

    for n in news:
        # print(n)
        print(‘**‘ * 5 + ‘列表页信息‘ + ‘**‘ * 10)
        print(‘新闻链接：‘ + n.a.attrs[‘href‘])
        print(‘新闻标题：‘ + n.select(‘.news-list-title‘)[0].text)
        print(‘新闻描述：‘ + n.a.select(‘.news-list-description‘)[0].text)
        print(‘新闻时间：‘ + n.a.select(‘.news-list-info > span‘)[0].text)
        print(‘新闻来源：‘ + n.a.select(‘.news-list-info > span‘)[1].text)
        news = getNewDetail(n.a.attrs[‘href‘])
        news[‘标题‘] = n.select(‘.news-list-title‘)[0].text
        news_list.append(news)
    return news_list


def getNewDetail(href):
    print(‘**‘ * 5 + ‘详情页信息‘ + ‘**‘ * 10)
    print(href)
    res1 = requests.get(href)
    res1.encoding = ‘UTF-8‘
    soup1 = BeautifulSoup(res1.text, ‘html.parser‘)
    news = {}
    if soup1.select(‘#content‘):
        news_content = soup1.select(‘#content‘)[0].text
        news[‘内容‘] = news_content
        print(news_content)  # 文章内容
    else:
        news[‘内容‘] = ‘‘
    if soup1.select(‘.show-info‘):  # 防止之前网页没有show_info
        news_info = soup1.select(‘.show-info‘)[0].text
    else:
        return news
    info_list = [‘来源‘, ‘发布时间‘, ‘点击‘, ‘作者‘, ‘审核‘, ‘摄影‘]  # 需要解析的字段
    news_info_set = set(news_info.split(‘\xa0‘)) - {‘ ‘, ‘‘}  # 网页中的 获取后会解析成\xa0，所以可以使用\xa0作为分隔符
    # 循环打印文章信息
    for n_i in news_info_set:
        for info_flag in info_list:
            if n_i.find(info_flag) != -1:  # 因为时间的冒号采用了英文符所以要进行判断
                if info_flag == ‘发布时间‘:
                    # 将发布时间字符串转为datetime格式,方便日后存储到数据库
                    release_time = datetime.strptime(n_i[n_i.index(‘:‘) + 1:], ‘%Y-%m-%d %H:%M:%S ‘)
                    news[info_flag] = release_time
                    print(info_flag + ‘:‘, release_time)
                elif info_flag == ‘点击‘:  # 点击次数是通过文章id访问php后使用js写入，所以这里单独处理
                    news[info_flag] = getClickCount(href)
                else:
                    news[info_flag] = n_i[n_i.index(‘：‘) + 1:]
                    print(info_flag + ‘:‘ + n_i[n_i.index(‘：‘) + 1:])
    print(‘————‘ * 40)
    return news


def getClickCount(news_url):
    click_num_url = ‘http://oa.gzcc.cn/api.php?op=count&id={}&modelid=80‘
    click_num_url = click_num_url.format(re.search(‘_(.*)/(.*).html‘, news_url).group(2))
    res2 = requests.get(click_num_url)
    res2.encoding = ‘UTF-8‘
    click_num = re.search("\$\(‘#hits‘\).html\(‘(\d*)‘\)", res2.text).group(1)
    print(‘点击:‘ + click_num)
    return click_num


print(crawlOnePageSchoolNews(‘http://news.gzcc.cn/html/xiaoyuanxinwen/‘))

pageURL = ‘http://news.gzcc.cn/html/xiaoyuanxinwen/{}.html‘
res = requests.get(‘http://news.gzcc.cn/html/xiaoyuanxinwen/‘)
res.encoding = ‘UTF-8‘
soup = BeautifulSoup(res.text, ‘html.parser‘)
newsSum = int(re.search(‘(\d*)条‘, soup.select(‘a.a1‘)[0].text).group(1))
if newsSum % 10:
    pageSum = int(newsSum / 10) + 1
else:
    pageSum = int(newsSum / 10)

for i in range(2, pageSum + 1):
    crawlOnePageSchoolNews(pageURL.format(i))


dit = pandas.DataFrame(news_list)
dit.to_excel(‘test.xlsx‘)
dit.to_csv(‘test.csv‘)

print(dit[[‘作者‘, ‘来源‘]][:6])
print(dit[(dit[‘来源‘] == ‘学校综合办‘) & (dit[‘点击‘] > 3000)])
print(dit[dit[‘来源‘].isin([‘国际学院‘, ‘学生工作处‘])])