一个完整的大作业

1.选一个自己感兴趣的主题。

选择广州商学院网页，网址为http://news.gzcc.cn/html/xiaoyuanxinwen/

2.网络上爬取相关的数据。

import requests
import re
from bs4 import BeautifulSoup
url='http://news.gzcc.cn/html/xiaoyuanxinwen/'
res=requests.get(url)
res.encoding='utf-8'
soup=BeautifulSoup(res.text,'html.parser')

#获取点击次数
def getclick(newurl):
    id=re.search('_(.*).html',newurl).group(1).split('/')[1]
    clickurl='http://oa.gzcc.cn/api.php?op=count&id={}&modelid=80'.format(id)
    click=int(requests.get(clickurl).text.split(".")[-1].lstrip("html('").rstrip("');"))
    return click

#获取内容
def getonpages(listurl):
    res=requests.get(listurl)
    res.encoding='utf-8'
    soup=BeautifulSoup(res.text,'html.parser')
    
    for news in soup.select('li'):
        if len(news.select('.news-list-title'))>0:
            title=news.select('.news-list-title')[0].text #标题
            time=news.select('.news-list-info')[0].contents[0].text#时间
            url1=news.select('a')[0]['href'] #url
            bumen=news.select('.news-list-info')[0].contents[1].text#部门
            description=news.select('.news-list-description')[0].text #描述

            resd=requests.get(url1)
            resd.encoding='utf-8'
            soupd=BeautifulSoup(resd.text,'html.parser')
            detail=soupd.select('.show-content')[0].text

            click=getclick(url1) #调用点击次数
            print(title,click)


count=int(soup.select('.a1')[0].text.rstrip("条"))
pages=count//10+1
for i in range(2,4):
    pagesurl="http://news.gzcc.cn/html/xiaoyuanxinwen/{}.html".format(i)
    getonpages(pagesurl)

获得数据

import requests
import re
import pandas
from bs4 import BeautifulSoup

url = 'http://news.gzcc.cn/html/xiaoyuanxinwen/'
res = requests.get(url)
res.encoding = 'utf-8'
soup = BeautifulSoup(res.text, 'html.parser')


# 获取点击次数
def getclick(newurl):
    id = re.search('_(.*).html', newurl).group(1).split('/')[1]
    clickurl = 'http://oa.gzcc.cn/api.php?op=count&id={}&modelid=80'.format(id)
    click = int(requests.get(clickurl).text.split(".")[-1].lstrip("html('").rstrip("');"))
    return click


# 给定单挑新闻链接，返回新闻细节的字典
def getdetail(listurl):
    res = requests.get(listurl)
    res.encoding = 'utf-8'
    soup = BeautifulSoup(res.text, 'html.parser')
    news={}
    news['url']=url
    news['title']=soup.select('.show-title')[0].text
    info = soup.select('.show-info')[0].text
    #news['dt']=datetime.strptime(info.lstrip('发布时间')[0:19],'%Y-%m-%d %H:%M:')
    #news['source']=re.search('来源:(.*)点击',info).group(1).strip()
    news['content']=soup.select('.show-content')[0].text.strip()
    news['click']=getclick(listurl)
    return (news)
#print(getonpages('http://news.gzcc.cn/html/2017/xiaoyuanxinwen_1017/8338.html'))

#给定新闻列表页的链接，返回所有新闻细节字典
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=[]
for i in range(2,3):
    listurl='http://news.gzcc.cn/html/xiaoyuanxinwen/'
    newstotal.extend(onepage(listurl))
#print(len(newstotal))
df =pandas.DataFrame(newstotal)
#print(df.head())
#print(df['title'])
df.to_excel('gzccnews.xlsx')
#with aqlite3.connect('gzccnewsdb2.sqlite') as db:
#df.to_sql('gzccnewdb2',con=db)

3.进行文本分析，生成词云。

import jieba
output = open('D:\dd.txt', 'a',encoding='utf-8')
txt = open('D:\bb.txt',"r",encoding='utf-8').read()
#去除一些出现频率较高且意义不大的词
ex = {'我院','我校','学院','一行'}
 
ls = []
words = jieba.lcut(txt)
counts = {}
for word in words:
    ls.append(word)
    if len(word) == 1 or word in ex:
        continue
    else:
        counts[word] = counts.get(word,0)+1
#for word in ex:
#    del(word)
items = list(counts.items())
items.sort(key = lambda x:x[1], reverse = True)
for i in range(100):
    word , count = items[i]
    print ("{:<10}{:>5}".format(word,count))
    output.write("{:<10}{:>5}".format(word,count) + '
')
output.close()

#coding:utf-8
import jieba
from wordcloud import WordCloud
import matplotlib.pyplot as plt
 
text =open("D:\cc.txt",'r',encoding='utf-8').read()
print(text)
wordlist = jieba.cut(text,cut_all=True)
wl_split = "/".join(wordlist)
 
mywc = WordCloud().generate(text)
plt.imshow(mywc)
plt.axis("off")
plt.show()

4.对文本分析结果解释说明。

根据词云分析可知学校的网址以工作与活动为主。

5.写一篇完整的博客，附上源代码、数据爬取及分析结果，形成一个可展示的成果。