简单微博爬取以及分析

对微博评论进行简单爬取并进行分析
任务：
1.爬取评论和时间（request和re）
2.词频统计（jieba）
3.词云展示（wordcloud）
4.时间分布（matplotlib）
代码如下：

#heheyang
import requests
import re
import jieba
import wordcloud
import time as ti
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


#数据爬取
start_url='weibo_path&&page='
header = {
     'cookie':'your cookie',
          'user-agent':'your agent'}
f=open('path','w',encoding='utf-8')
#f.write('time\tcomment\n')
all_comment=''
all_time=[]
for i in range(1,51 ):
    #print('正在爬取第%d页....' %i)
    url=start_url+str(i)
    r = requests.get(url,timeout=30,headers = header)
    r.raise_for_status()
    r.encoding='utf-8'
    if i==1:
        comment=re.findall('(.*?)',r.text)
        time=re.findall('(.*?) ',r.text)
        del comment[0:4]
        del time[0:4]
    else:
        comment = re.findall('(.*?)', r.text)
        time = re.findall('(.*?) ', r.text)
    for i in range(len(comment)):
        f.write(time[i]+'\t')
        f.write(comment[i]+'\n')
        all_comment+=comment[i]
        all_time.append(time[i])
f.close()
#print(all_comment)
#print(all_time)
#评论词云展示
pattern="[\u4e00-\u9fa5]+"
regex = re.compile(pattern)
comment_chinese=regex.findall(all_comment)
text=''
for i in comment_chinese:
    text+=i
#词频统计
words=jieba.lcut(text)
counts={
     }
for word in words:
    if word not in counts:
        counts[word]=1
    else:
        counts[word]+=1
words_counts=list(counts.items())
words_counts.sort(key=lambda x:x[1],reverse=True)
#格式输出
tplt="{0:^10}\t{1:^10}\t"
print(tplt.format("word", "      count"))
for i in range(10):
    word,count=words_counts[i]
    #输出前10
    print(tplt.format(word,count))
#词云展示
cut=' '.join(words)
w=wordcloud.WordCloud(font_path='msyh.ttc',collocations=False,height=600, width=1000,background_color='white')
w.generate(cut)
w.to_file('word.png')

#热度分析
time_list=[]
for i in all_time:
    i=i.replace('月','-')
    i=i.replace('日','')
    i='2020-'+i+':00'
    ts=int(ti.mktime(ti.strptime(''.join(i), "%Y-%m-%d %H:%M:%S")))#转换为时间戳
    time_list.append(ts)
dict={
     
    'timeStamp':time_list
}
df=pd.DataFrame(dict)
mean=df['timeStamp'].mean()
std=df['timeStamp'].std()
x=np.arange(1591889940,1593856680,0.1)
y=np.exp(-((x-mean) ** 2) / (2 * std ** 2)) / (std * np.sqrt(2 * np.pi))
plt.plot(x,y)
#plt.hist(df['timeStamp'], bins=12, rwidth=0.9, density=True)
plt.title('time distribution')
plt.xlabel('Time')
plt.ylabel('Attention to events')
plt.savefig('time distribution.png')
plt.show()

爬虫写得不多，小白学习ing，欢迎大家交流…