爬取数据

huputitle_spiders.py

#coding:utf-8    
import scrapy   
from huputitle.items import HuputitleItem

from scrapy.crawler import CrawlerProcess 

class hupuSpider(scrapy.Spider):
    name = 'huputitle'    
    allowed_domains = ["bbs.hupu.com"]    
    start_urls = ["https://bbs.hupu.com/bxj"]    
         
    def parse(self, response):    
        item = HuputitleItem()    
        item['titles'] = response.xpath('//a[@id=""]/text()').extract()#提取标题
        # print 'titles',item['titles']  
        yield item 
        new_url = "https://bbs.hupu.com" + response.xpath('//a[@id="j_next"]/@href').extract_first()
        if new_url:    
            yield scrapy.Request(new_url,callback=self.parse)  

items.py

# -*- coding: utf-8 -*-
import scrapy


class HuputitleItem(scrapy.Item):
    # define the fields for your item here like:
    titles = scrapy.Field()

pipelines.py

# -*- coding: utf-8 -*-
import os    
import urllib   
  
from huputitle import settings  

import sys  
reload(sys)  
sys.setdefaultencoding( "utf-8" )  

class HuputitlePipeline(object):
    def process_item(self, item, spider):
        for title in item['titles']:
            # print 'title',title
            fo = open("foo.txt", "a")
            fo.write("".join(title)+"\r\n")
        fo.close()
        return item

settings.py

BOT_NAME = 'huputitle'

SPIDER_MODULES = ['huputitle.spiders']
NEWSPIDER_MODULE = 'huputitle.spiders'

ITEM_PIPELINES = {  
    'huputitle.pipelines.HuputitlePipeline': 1,  
}

USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_3) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.54 Safari/536.5'

最终爬取了100页2W多个标题

分词并统计词的数量

这里我使用了 jieba 这个库来分词
hupudivide.py

#encoding=utf-8  
import jieba  
import sys
reload(sys)
sys.setdefaultencoding('utf-8')

fo = open("hupu.txt", "r")
fi = open("hupudi.txt", "w")
lines = fo.readlines()
for line in lines:
    seg_list = jieba.cut_for_search(line)
    fi.write(" \n".join(seg_list)) 

分出了17w个词

然后统计数量
huPuCounter.py

#encoding=utf-8  
import jieba  
import jieba.analyse
import time
from collections import Counter
import sys
reload(sys)
sys.setdefaultencoding('utf-8')

fo = open("hupudi.txt", "r")
fi = open("hupunum.txt", "w")
fl = open("hupunumword.txt", "w")
f = open("hupuword.txt", "w")

lines = fo.readlines()

d = {}

for line in lines:
    if line not in d:
        d[line] = 1
    else:
        d[line] = d[line] + 1

d = sorted(d.items(),key=lambda item:item[1],reverse=True)

for k in d:
    fi.write("%s%d\n" % (k[0][:-1].encode('utf-8'),k[1]))
    if len(k[0][:-1].encode('utf-8')) >= 6:
        fl.write("%s%d\n" % (k[0][:-1].encode('utf-8'),k[1]))
        f.write("%s" % (k[0][:-1].encode('utf-8')))

这里我统计了两个词以下和两个词以上的词的量分配如图

生成词云以及其他数据图表

makeHupuCloud.py

#encoding=utf-8 
import matplotlib.pyplot as plt
from wordcloud import WordCloud
import jieba

text_from_file_with_apath = open('foo.txt').read()

wordlist_after_jieba = jieba.cut(text_from_file_with_apath, cut_all = False)
wl_space_split = " ".join(wordlist_after_jieba)

backgroud_Image = plt.imread('huputag.jpg')
my_wordcloud = WordCloud(background_color = 'white',    
                mask = backgroud_Image,  ).generate(wl_space_split)

plt.imshow(my_wordcloud)
plt.axis("off")
plt.show()

这里我是用python的wordcloud库生成的词云，图片是hupu的logo


使用jieba的分词分出词性 生成的图表