一個完整的python大做業

因爲能選擇一個感興趣的網站進行數據分析,因此此次選擇爬取的網站是新華網,其網址爲"http://www.xinhuanet.com/",而後對其進行數據分析並生成詞雲css

運行整個程序相關的代碼包html

import requests
import re
from bs4 import BeautifulSoup
from datetime import datetime
import pandas
import sqlite3
import jieba
from wordcloud import WordCloud
import matplotlib.pyplot as plt

爬取網頁信息sql

url = "http://www.xinhuanet.com/"

f=open("css.txt","w+")
res0 = requests.get(url)
res0.encoding="utf-8"
soup = BeautifulSoup(res0.text,"html.parser")
newsgroup=[]
for news in soup.select("li"):
    if len(news.select("a"))>0:
        print(news.select("a")[0].text)
        title=news.select("a")[0].text
        f.write(title)
f.close()

存入txt文件中,並進行字詞統計數據庫

f0 = open('css.txt','r')
qz=[]
qz=f0.read()
f0.close()
print(qz)

words = list(jieba.cut(qz))

ul={':','','"','','','','','','','',' ','\u3000','','\n'}
dic={}

keys = set(words)-ul
for i in keys:
    dic[i]=words.count(i)

c = list(dic.items())
c.sort(key=lambda x:x[1],reverse=True)

f1 = open('diectory.txt','w')
for i in range(10):
    print(c[i])
    for words_count in range(c[i][1]):
        f1.write(c[i][0]+' ')
f1.close()

存入數據庫網站

df = pandas.DataFrame(words)

print(df.head())

with sqlite3.connect('newsdb3.sqlite') as db:

    df.to_sql('newsdb3',con = db)

製做詞雲url

f3 = open('diectory.txt','r')
cy_file = f3.read()
f3.close()
cy = WordCloud().generate(cy_file)
plt.imshow(cy)
plt.axis("off")
plt.show()

最終成果spa

 完整代碼code

import requests
import re
from bs4 import BeautifulSoup
from datetime import datetime
import pandas
import sqlite3
import jieba
from wordcloud import WordCloud
import matplotlib.pyplot as plt


url = "http://www.xinhuanet.com/"

    

f=open("css.txt","w+")
res0 = requests.get(url)
res0.encoding="utf-8"
soup = BeautifulSoup(res0.text,"html.parser")
newsgroup=[]
for news in soup.select("li"):
    if len(news.select("a"))>0:
        print(news.select("a")[0].text)
        title=news.select("a")[0].text
        f.write(title)
f.close()

f0 = open('css.txt','r')
qz=[]
qz=f0.read()
f0.close()
print(qz)

words = list(jieba.cut(qz))

ul={':','','"','','','','','','','',' ','\u3000','','\n'}
dic={}

keys = set(words)-ul
for i in keys:
    dic[i]=words.count(i)

c = list(dic.items())
c.sort(key=lambda x:x[1],reverse=True)

f1 = open('diectory.txt','w')
for i in range(10):
    print(c[i])
    for words_count in range(c[i][1]):
        f1.write(c[i][0]+' ')
f1.close()

df = pandas.DataFrame(words)

print(df.head())

with sqlite3.connect('newsdb3.sqlite') as db:

    df.to_sql('newsdb3',con = db)


f3 = open('diectory.txt','r')
cy_file = f3.read()
f3.close()
cy = WordCloud().generate(cy_file)
plt.imshow(cy)
plt.axis("off")
plt.show()
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