www cti struct 解析 tel lte name width oat
参数解析
itemId是商品ID, sellerId 是卖家ID, currentPage是当前页码,目标url是https://rate.tmall.com/list_detail_rate.htm?itemId=15332134505&spuId=294841&sellerId=917264765&order=3¤tPage=1
正则解析
1.cnt字符串不要随便换行(否则可能报错:SyntaxError: EOL while scanning string literal),
2.findall(正则规则,字符串) 方法能够以列表的形式返回能匹配的字符串
- #coding=utf-8
- import re
- cnt = ‘"aliMallSeller":False,"anony":True,"appendComment":"","attributes":"","attributesMap":"","aucNumId":"","auctionPicUrl":"","auctionPrice":"","auctionSku":"化妆品净含量:75ml","auctionTitle":"","buyCount":0,"carServiceLocation":"","cmsSource":"天猫","displayRatePic":"","displayRateSum":0,"displayUserLink":"","displayUserNick":"t***凯","displayUserNumId":"","displayUserRateLink":"","dsr":0.0,"fromMall":True,"fromMemory":0,"gmtCreateTime":1504930533000,"goldUser":False,"id":322848226237,"pics":["//img.alicdn.com/bao/uploaded/i3/2699329812/TB2hr6keQ.HL1JjSZFlXXaiRFXa_!!0-rate.jpg"],"piCSSmall":"","position":"920-11-18,20;","rateContent":"送了面膜 和晶莹水 skii就是A 不错","rateDate":"2017-09-09 12:15:33","reply":"一次偶然的机会,遇见了亲,一次偶然的机会,亲选择了SK-II,生命中有太多的选择,亲的每一次选择都是一种缘分。让SK-II与您形影不离,任岁月洗礼而秀美如初~每日清晨拉开窗帘迎来的不仅止破晓曙光,还有崭新的自己~【SK-II官方旗舰店Lily】","sellerId":917264765,"serviceRateContent":"","structuredRateList":[],"tamllSweetLevel":3,"tmallSweetPic":"tmall-grade-t3-18.png","tradeEndTime":1504847657000,"tradeId":"","useful":True,"userIdEncryption":"","userInfo":"","userVipLevel":0,"userVipPic":""‘
- nickname = []
- regex = re.compile(‘"displayUserNick":"(.*?)"‘)
- print regex
- nk = re.findall(regex,cnt)
- for i in nk:
- print i
- nickname.extend(nk)
- print nickname
- ak = re.findall(‘"auctionSku":"(.*?)"‘,cnt)
- for j in ak:
- print j
- rc = re.findall(‘"rateContent":"(.*?)"‘,cnt)
- for n in rc:
- print n
- rd = re.findall(‘"rateDate":"(.*?)"‘,cnt)
- for m in rd:
- print m
输出:
完整源码
参考:http://www.jianshu.com/p/632a3d3b15c2
- #coding=utf-8
- import requests
- import re
- import sys
- reload(sys)
- sys.setdefaultencoding(‘utf-8‘)
- #urls = []
- #for i in list(range(1,500)):
- # urls.append(‘https://rate.tmall.com/list_detail_rate.htm?itemId=15332134505&spuId=294841&sellerId=917264765&order=1¤tPage=%s‘%i)
- tmpt_url = ‘https://rate.tmall.com/list_detail_rate.htm?itemId=15332134505&spuId=294841&sellerId=917264765&order=1¤tPage=%d‘
- urllist = [tmpt_url%i for i in range(1,100)]
- #print urllist
- nickname = []
- auctionSku = []
- ratecontent = []
- ratedate = []
- headers = ‘‘
- for url in urllist:
- content = requests.get(url).text
- nk = re.findall(‘"displayUserNick":"(.*?)"‘,content) #findall(正则规则,字符串) 方法能够以列表的形式返回能匹配的字符串
- #print nk
- nickname.extend(nk)
- auctionSku.extend(re.findall(‘"auctionSku":"(.*?)"‘,content))
- ratecontent.extend(re.findall(‘"rateContent":"(.*?)"‘,content))
- ratedate.extend(re.findall(‘"rateDate":"(.*?)"‘,content))
- print (nickname,ratedate)
- for i in list(range(0,len(nickname))):
- text =‘,‘.join((nickname[i],ratedate[i],auctionSku[i],ratecontent[i]))+‘\n‘
- with open(r"C:\Users\HP\Desktop\codes\DATA\SK-II_TmallContent.csv",‘a+‘) as file:
- file.write(text+‘ ‘)
- print("写入成功")
注:url每次遍历,正则匹配的数据都不止一个,所以使用extend追加而不是append
输出:
1.要不要买——评论分析
- import pandas as pd
- from pandas import Series,DataFrame
- import jieba
- from collections import Counter
- df = pd.read_csv(r‘C:/Users/HP/Desktop/codes/DATA/SK-II_TmallContent.csv‘,encoding=‘gbk‘) #否则中文乱码
- #print df.columns
- df.columns = [‘useName‘,‘date‘,‘type‘,‘content‘]
- #print df[:10]
- tlist = Series.as_matrix(df[‘content‘]).tolist()
- text = [i for i in tlist if type(i)!= float] #if type(i)!= float一定得加不然报错
- text = ‘ ‘.join(text)
- #print text
- wordlist_jieba = jieba.cut(text,cut_all=True)
- stoplist = {}.fromkeys([u‘的‘, u‘了‘, u‘是‘,u‘有‘]) #自定义中文停词表,注意得是unicode
- print stoplist
- wordlist_jieba = [i for i in wordlist_jieba if i not in stoplist] #and len(i) > 1
- #print u"[全模式]: ", "/ ".join(wordlist_jieba)
- count = Counter(wordlist_jieba) #统计出现次数,以字典的键值对形式存储,元素作为key,其计数作为value。
- result = sorted(count.items(), key=lambda x: x[1], reverse=True) #key=lambda x: x[1]在此表示用次数作为关键字
- for word in result:
- print word[0], word[1]
- from pyecharts import WordCloud
- data = dict(result[:100])
- wordcloud = WordCloud(‘高频词云‘,width = 800,height = 600)
- wordcloud.add(‘ryana‘,data.keys(),data.values(),word_size_range = [30,300])
- wordcloud
输出:
好用的频率占据榜首,只是不明白为什么要切分
2.买什么——类型分析
- import pandas as pd
- from pandas import Series,DataFrame
- df = pd.read_csv(r‘C:/Users/HP/Desktop/codes/DATA/SK-II_TmallContent.csv‘,encoding=‘gbk‘) #否则中文乱码
- #print df.columns
- df.columns = [‘useName‘,‘date‘,‘type‘,‘content‘]
- print df[:5]
- from pyecharts import Pie
- pie = Pie(‘净含量购买分布‘)
- v = df[‘type‘].tolist()
- print v[:5]
- #n1 = v.count(u‘\u5316\u5986\u54c1\u51c0\u542b\u91cf:230ml‘)
- n1 = v.count(u‘化妆品净含量:75ml‘)
- n2 = v.count(u‘化妆品净含量:160ml‘)
- n3 = v.count(u‘化妆品净含量:230ml‘)
- n4 = v.count(u‘化妆品净含量:330ml‘)
- #print n1,n2,n3,n4 #800 87 808 124
- N = [n1,n2,n3,n4]
- #print N #[800,87,808,124]
- attr = [‘体验装‘,‘畅销经典‘,‘忠粉挚爱‘,‘屯货之选‘]
- pie.add(‘ryana‘,attr,N,is_label_show = True)
- pie
输出:
练习:天猫商品决策分析
来源: http://www.bubuko.com/infodetail-2329582.html