标签：count ... www 电子商务 cn index 用户 第六周 http
访问数据库
import os
import pandas as pd
# 修改工作路径到指定文件夹
#os.chdir("D:/chapter11/demo")
# 第一种连接方式
#from sqlalchemy import create_engine
#engine = create_engine('mysql+pymysql://root:[email protected]:3306/test?charset=utf8')
#sql = pd.read_sql('all_gzdata', engine, chunksize = 10000)
# 第二种连接方式
import pymysql as pm
con = pm.connect(host='localhost',user='root',password='123456',database='test',charset='utf8')
data = pd.read_sql('select * from all_gzdata',con=con)
con.close() #关闭连接
# 保存读取的数据
data.to_csv("D:\\360MoveData\\Users\\86130\\Documents\\Tencent Files\\2268756693\\FileRecv\\all_gzdata.csv", index=False, encoding='utf-8')
网页类型设计
import pandas as pd
from sqlalchemy import create_engine
engine = create_engine('mysql+pymysql://root:[email protected]:3306/test?charset=utf8')
sql = pd.read_sql('all_gzdata', engine, chunksize = 10000)
# 分析网页类型
counts = [i['fullURLId'].value_counts() for i in sql] #逐块统计
counts = counts.copy()
counts = pd.concat(counts).groupby(level=0).sum() # 合并统计结果，把相同的统计项合并（即按index分组并求和）
counts = counts.reset_index() # 重新设置index，将原来的index作为counts的一列。
counts.columns = ['index', 'num'] # 重新设置列名，主要是第二列，默认为0
counts['type'] = counts['index'].str.extract('(\d{3})') # 提取前三个数字作为类别id
counts_ = counts[['type', 'num']].groupby('type').sum() # 按类别合并
counts_.sort_values(by='num', ascending=False, inplace=True) # 降序排列
counts_['ratio'] = counts_.iloc[:,0] / counts_.iloc[:,0].sum()
print(counts_)
num ratio
type
101 411665 0.491570
199 201426 0.240523
107 182900 0.218401
301 18430 0.022007
102 17357 0.020726
106 3957 0.004725
103 1715 0.002048
知识类型内部统计
# 因为只有107001一类，但是可以继续细分成三类：知识内容页、知识列表页、知识首页
def count107(i): #自定义统计函数
j = i[['fullURL']][i['fullURLId'].str.contains('107')].copy() # 找出类别包含107的网址
j['type'] = None # 添加空列
j['type'][j['fullURL'].str.contains('info/.+?/')]= '知识首页'
j['type'][j['fullURL'].str.contains('info/.+?/.+?')]= '知识列表页'
j['type'][j['fullURL'].str.contains('/\d+?_*\d+?\.html')]= '知识内容页'
return j['type'].value_counts()
# 注意：获取一次sql对象就需要重新访问一下数据库(!!!)
#engine = create_engine('mysql+pymysql://root:[email protected]:3306/test?charset=utf8')
sql = pd.read_sql('all_gzdata', engine, chunksize = 10000)
counts2 = [count107(i) for i in sql] # 逐块统计
counts2 = pd.concat(counts2).groupby(level=0).sum() # 合并统计结果
print(counts2)
#计算各个部分的占比
res107 = pd.DataFrame(counts2)
# res107.reset_index(inplace=True)
res107.index.name= '107类型'
res107.rename(columns={'type':'num'}, inplace=True)
res107['比例'] = res107['num'] / res107['num'].sum()
res107.reset_index(inplace = True)
print(res107)
知识内容页 164243
知识列表页 9656
知识首页 9001
Name: type, dtype: int64
107类型 num 比例
0 知识内容页 164243 0.897993
1 知识列表页 9656 0.052794
2 知识首页 9001 0.049213
统计带“?”的数据
def countquestion(i): # 自定义统计函数
j = i[['fullURLId']][i['fullURL'].str.contains('\?')].copy() # 找出类别包含107的网址
return j
# 注意获取一次sql对象就需要重新访问一下数据库
engine = create_engine('mysql+pymysql://root:[email protected]:3306/test?charset=utf8')
sql = pd.read_sql('all_gzdata', engine, chunksize = 10000)
counts3 = [countquestion(i)['fullURLId'].value_counts() for i in sql]
counts3 = pd.concat(counts3).groupby(level=0).sum()
print(counts3)
# 求各个类型的占比并保存数据
df1 = pd.DataFrame(counts3)
df1['perc'] = df1['fullURLId']/df1['fullURLId'].sum()*100
df1.sort_values(by='fullURLId',ascending=False,inplace=True)
print(df1.round(4))
101003 47
102002 25
107001 346
1999001 64718
301001 356
Name: fullURLId, dtype: int64
fullURLId perc
1999001 64718 98.8182
301001 356 0.5436
107001 346 0.5283
101003 47 0.0718
102002 25 0.0382
统计199类型中的具体类型占比
def page199(i): #自定义统计函数
j = i[['fullURL','pageTitle']][(i['fullURLId'].str.contains('199')) &
(i['fullURL'].str.contains('\?'))]
j['pageTitle'].fillna('空',inplace=True)
j['type'] = '其他' # 添加空列
j['type'][j['pageTitle'].str.contains('法律快车-律师助手')]= '法律快车-律师助手'
j['type'][j['pageTitle'].str.contains('咨询发布成功')]= '咨询发布成功'
j['type'][j['pageTitle'].str.contains('免费发布法律咨询' )] = '免费发布法律咨询'
j['type'][j['pageTitle'].str.contains('法律快搜')] = '快搜'
j['type'][j['pageTitle'].str.contains('法律快车法律经验')] = '法律快车法律经验'
j['type'][j['pageTitle'].str.contains('法律快车法律咨询')] = '法律快车法律咨询'
j['type'][(j['pageTitle'].str.contains('_法律快车')) |
(j['pageTitle'].str.contains('-法律快车'))] = '法律快车'
j['type'][j['pageTitle'].str.contains('空')] = '空'
return j
# 注意：获取一次sql对象就需要重新访问一下数据库
#engine = create_engine('mysql+pymysql://root:[email protected]:3306/test?charset=utf8')
sql = pd.read_sql('all_gzdata', engine, chunksize = 10000)# 分块读取数据库信息
#sql = pd.read_sql_query('select * from all_gzdata limit 10000', con=engine)
counts4 = [page199(i) for i in sql] # 逐块统计
counts4 = pd.concat(counts4)
d1 = counts4['type'].value_counts()
print(d1)
d2 = counts4[counts4['type']=='其他']
print(d2)
# 求各个部分的占比并保存数据
df1_ = pd.DataFrame(d1)
df1_['perc'] = df1_['type']/df1_['type'].sum()*100
df1_.sort_values(by='type',ascending=False,inplace=True)
print(df1_)
法律快车-律师助手 49894
法律快车法律咨询 6421
咨询发布成功 5220
快搜 1943
法律快车 818
其他 359
法律快车法律经验 59
空 4
Name: type, dtype: int64
fullURL \
2631 http://www.lawtime.cn/spelawyer/index.php?py=g...
2632 http://www.lawtime.cn/spelawyer/index.php?py=g...
1677 http://m.baidu.com/from=844b/bd_page_type=1/ss...
4303 http://m.baidu.com/from=0/bd_page_type=1/ssid=...
3673 http://www.lawtime.cn/lawyer/lll25879862593080...
... ...
4829 http://www.lawtime.cn/spelawyer/index.php?m=se...
4837 http://www.lawtime.cn/spelawyer/index.php?m=se...
4842 http://www.lawtime.cn/spelawyer/index.php?m=se...
8302 http://www.lawtime.cn/spelawyer/index.php?m=se...
5034 http://www.baidu.com/link?url=O7iBD2KmoJdkHWTZ...
pageTitle type
2631 个旧律师成功案例 - 法律快车提供个旧知名律师、优秀律师、专业律师的咨询和推荐 其他
2632 个旧律师成功案例 - 法律快车提供个旧知名律师、优秀律师、专业律师的咨询和推荐 其他
1677 婚姻法论文 - 法律快车法律论文 其他
4303 什么是机动车？什么是非机动车？ - 法律快车交通事故 其他
3673 404错误提示页面 - 法律快车 其他
... ... ...
4829 律师搜索,律师查找 - 法律快车提供全国知名律师、优秀律师、专业律师的咨询和推荐 其他
4837 律师搜索,律师查找 - 法律快车提供全国知名律师、优秀律师、专业律师的咨询和推荐 其他
4842 律师搜索,律师查找 - 法律快车提供全国知名律师、优秀律师、专业律师的咨询和推荐 其他
8302 律师搜索,律师查找 - 法律快车提供全国知名律师、优秀律师、专业律师的咨询和推荐 其他
5034 离婚协议书范本（2015年版） - 法律快车婚姻法 其他
[359 rows x 3 columns]
type perc
法律快车-律师助手 49894 77.094471
法律快车法律咨询 6421 9.921506
咨询发布成功 5220 8.065762
快搜 1943 3.002256
法律快车 818 1.263945
其他 359 0.554714
法律快车法律经验 59 0.091165
空 4 0.006181
统计无目的浏览用户中各个类型占比
def xiaguang(i): #自定义统计函数
j = i.loc[(i['fullURL'].str.contains('\.html'))==False,
['fullURL','fullURLId','pageTitle']]
return j
# 注意获取一次sql对象就需要重新访问一下数据库
engine = create_engine('mysql+pymysql://root:[email protected]:3306/test?charset=utf8')
sql = pd.read_sql('all_gzdata', engine, chunksize = 10000)# 分块读取数据库信息
counts5 = [xiaguang(i) for i in sql]
counts5 = pd.concat(counts5)
xg1 = counts5['fullURLId'].value_counts()
print(xg1)
# 求各个部分的占比
xg_ = pd.DataFrame(xg1)
xg_.reset_index(inplace=True)
xg_.columns= ['index', 'num']
xg_['perc'] = xg_['num']/xg_['num'].sum()*100
xg_.sort_values(by='num',ascending=False,inplace=True)
xg_['type'] = xg_['index'].str.extract('(\d{3})') #提取前三个数字作为类别id
xgs_ = xg_[['type', 'num']].groupby('type').sum() #按类别合并
xgs_.sort_values(by='num', ascending=False,inplace=True) #降序排列
xgs_['percentage'] = xgs_['num']/xgs_['num'].sum()*100
print(xgs_.round(4))
统计用户浏览网页次数的情况
# 统计点击次数
engine = create_engine('mysql+pymysql://root:[email protected]:3306/test?charset=utf8')
sql = pd.read_sql('all_gzdata', engine, chunksize = 10000)# 分块读取数据库信息
counts1 = [i['realIP'].value_counts() for i in sql] # 分块统计各个IP的出现次数
counts1 = pd.concat(counts1).groupby(level=0).sum() # 合并统计结果，level=0表示按照index分组
print(counts1)
counts1_ = pd.DataFrame(counts1)
counts1_
counts1['realIP'] = counts1.index.tolist()
counts1_[1]=1 # 添加1列全为1
hit_count = counts1_.groupby('realIP').sum() # 统计各个“不同点击次数”分别出现的次数
# 也可以使用counts1_['realIP'].value_counts()功能
hit_count.columns=['用户数']
hit_count.index.name = '点击次数'
# 统计1~7次、7次以上的用户人数
hit_count.sort_index(inplace = True)
hit_count_7 = hit_count.iloc[:7,:]
time = hit_count.iloc[7:,0].sum() # 统计点击次数7次以上的用户数
hit_count_7 = hit_count_7.append([{'用户数':time}], ignore_index=True)
hit_count_7.index = ['1','2','3','4','5','6','7','7次以上']
hit_count_7['用户比例'] = hit_count_7['用户数'] / hit_count_7['用户数'].sum()
print(hit_count_7)
82033 2
95502 1
103182 1
116010 2
136206 1
..
4294809358 2
4294811150 1
4294852154 3
4294865422 2
4294917690 1
Name: realIP, Length: 230149, dtype: int64
用户数 用户比例
1 132119 0.574059
2 44175 0.191941
3 17573 0.076355
4 10156 0.044128
5 5952 0.025862
6 4132 0.017954
7 2632 0.011436
7次以上 13410 0.058267
分析浏览一次的用户行为
# 初始化数据库连接:
engine = create_engine('mysql+pymysql://root:123456@localhost:3306/test?charset=utf8')
sql = pd.read_sql('all_gzdata', engine, chunksize=1024 * 5)
# 分块统计各个IP的点击次数
result = [i['realIP'].value_counts() for i in sql]
click_count = pd.concat(result).groupby(level=0).sum()
click_count = click_count.reset_index()
click_count.columns = ['realIP', 'times']
# 筛选出来点击一次的数据
click_one_data = click_count[click_count['times'] == 1]
# 这里只能再次读取数据 因为sql是一个生成器类型，所以在使用过一次以后，就不能继续使用了。必须要重新执行一次读取。
sql = pd.read_sql('all_gzdata', engine, chunksize=1024 * 5)
# 取出这三列数据
data = [i[['fullURLId', 'fullURL', 'realIP']] for i in sql]
data = pd.concat(data)
# 和并数据 我以click_one_data为基准 按照realIP合并过来，目的方便查看点击一次的网页和realIP
merge_data = pd.merge(click_one_data, data, on='realIP', how='left')
# 点击一次的数据统计 写入数据库 以方便读取 校准无误 写入后就可以注释掉此句代码
#erge_data.to_sql('click_one_count', engine, if_exists='append')
print(merge_data)
# 统计排名前4和其他的网页类型
URL_count_4 = URL_count.iloc[:4,:]
time = hit_count.iloc[4:,0].sum() # 统计其他的
URLindex = URL_count_4.index.values
URL_count_4 = URL_count_4.append([{'count':time}], ignore_index=True)
URL_count_4.index = [URLindex[0], URLindex[1], URLindex[2], URLindex[3],
'其他']
URL_count_4['比例'] = URL_count_4['count'] / URL_count_4['count'].sum()
print(URL_count_4)
realIP times fullURLId \
0 95502 1 101003
1 103182 1 101003
2 136206 1 101003
3 140151 1 107001
4 155761 1 101003
... ... ... ...
132114 4294737166 1 101003
132115 4294804343 1 101003
132116 4294807822 1 101003
132117 4294811150 1 101003
132118 4294917690 1 101003
fullURL
0 http://www.lawtime.cn/ask/question_7882607.html
1 http://www.lawtime.cn/ask/question_7174864.html
2 http://www.lawtime.cn/ask/question_8246285.html
3 http://www.lawtime.cn/info/gongsi/slbgfgs/2011...
4 http://www.lawtime.cn/ask/question_5951952.html
... ...
132114 http://www.lawtime.cn/ask/question_3947040.html
132115 http://www.lawtime.cn/ask/question_2064846.html
132116 http://www.lawtime.cn/ask/question_9981155.html
132117 http://www.lawtime.cn/ask/question_4931163.html
132118 http://www.lawtime.cn/ask/question_6910223.html
[132119 rows x 4 columns]
count 比例
101003 102560 0.649011
107001 19443 0.123037
1999001 9381 0.059364
301001 515 0.003259
其他 26126 0.165328
统计单用户浏览次数为一次的网页
# 在浏览1次的前提下, 得到的网页被浏览的总次数
fullURL_count = pd.DataFrame(real_one.groupby("fullURL")["fullURL"].count())
fullURL_count.columns = ["count"]
fullURL_count["fullURL"] = fullURL_count.index.tolist()
fullURL_count.sort_values(by='count', ascending=False, inplace=True) # 降序排列
# 网页类型ID统计
fullURLId_count = merge_data['fullURLId'].value_counts()
fullURLId_count = fullURLId_count.reset_index()
fullURLId_count.columns = ['fullURLId', 'count']
fullURLId_count['percent'] = fullURLId_count['count'] / fullURLId_count['count'].sum() * 100
print('*****' * 10)
print(fullURLId_count)
# 用户点击一次 浏览的网页统计
fullURL_count = merge_data['fullURL'].value_counts()
fullURL_count = fullURL_count.reset_index()
fullURL_count.columns = ['fullURL', 'count']
fullURL_count['percent'] = fullURL_count['count'] / fullURL_count['count'].sum() * 100
print('*****' * 10)
print(fullURL_count)
**************************************************
fullURLId count percent
0 101003 102560 77.626988
1 107001 19443 14.716279
2 1999001 9381 7.100417
3 301001 515 0.389800
4 102001 70 0.052983
5 103003 45 0.034060
6 101002 33 0.024977
7 101001 28 0.021193
8 102002 13 0.009840
9 106001 13 0.009840
10 101009 4 0.003028
11 101004 3 0.002271
12 101007 3 0.002271
13 101008 2 0.001514
14 102003 2 0.001514
15 101005 1 0.000757
16 102004 1 0.000757
17 101006 1 0.000757
18 102006 1 0.000757
**************************************************
fullURL count percent
0 http://www.lawtime.cn/info/shuifa/slb/20121119... 1013 0.766733
1 http://www.lawtime.cn/info/hunyin/lhlawlhxy/20... 501 0.379204
2 http://www.lawtime.cn/ask/question_925675.html 423 0.320166
3 http://www.lawtime.cn/info/shuifa/slb/20121119... 367 0.277780
4 http://www.lawtime.cn/ask/exp/13655.html 301 0.227825
... ... ... ...
88030 http://www.lawtime.cn/ask/question_3357263.html 1 0.000757
88031 http://www.lawtime.cn/info/laodong/laodonganli... 1 0.000757
88032 http://www.lawtime.cn/info/lunwen/ipzhuzuo/201... 1 0.000757
88033 http://www.lawtime.cn/ask/question_307554.html 1 0.000757
88034 http://www.lawtime.cn/ask/question_10467655.html 1 0.000757
[88035 rows x 3 columns]
删除不符合规则的网页
import os
import re
import pandas as pd
import pymysql as pm
from random import sample
# 修改工作路径到指定文件夹
os.chdir("D:\\360MoveData\\Users\\86130\\Documents\\Tencent Files\\2268756693\\FileRecv")
# 读取数据
con = pm.connect(host='localhost',user='root',password='123456',database='test',charset='utf8')
data = pd.read_sql('select * from all_gzdata',con=con)
con.close() # 关闭连接
# 取出107类型数据
index107 = [re.search('107',str(i))!=None for i in data.loc[:,'fullURLId']]
data_107 = data.loc[index107,:]
# 在107类型中筛选出婚姻类数据
index = [re.search('hunyin',str(i))!=None for i in data_107.loc[:,'fullURL']]
data_hunyin = data_107.loc[index,:]
# 提取所需字段(realIP、fullURL)
info = data_hunyin.loc[:,['realIP','fullURL']]
# 去除网址中“？”及其后面内容
da = [re.sub('\?.*','',str(i)) for i in info.loc[:,'fullURL']]
info.loc[:,'fullURL'] = da # 将info中‘fullURL’那列换成da
# 去除无html网址
index = [re.search('\.html',str(i))!=None for i in info.loc[:,'fullURL']]
index.count(True) # True 或者 1 ， False 或者 0
info1 = info.loc[index,:]
print("（学号 3110）去除无html网址如下：")
print(info1)
realIP fullURL
0 2683657840 http://www.lawtime.cn/info/hunyin/hunyinfagui/...
4 2683657840 http://www.lawtime.cn/info/hunyin/hunyinfagui/...
9 1275347569 http://www.lawtime.cn/info/hunyin/lhlawlhxy/20...
62 1531496412 http://www.lawtime.cn/info/hunyin/hunyinfagui/...
86 838215995 http://www.lawtime.cn/info/hunyin/lhlawlhxy/20...
... ... ...
837347 2320911216 http://www.lawtime.cn/info/hunyin/lhlawlhxy/20...
837362 3458366734 http://www.lawtime.cn/info/hunyin/jhsy/daiyun/...
837370 2526756791 http://www.lawtime.cn/info/hunyin/hynews/20101...
837376 4267065457 http://www.lawtime.cn/info/hunyin/lhlawlhxy/20...
837434 3271035001 http://www.lawtime.cn/info/hunyin/lhlawlhxy/20...
[31199 rows x 2 columns]
还原翻译网址
# 找出翻页和非翻页网址
index = [re.search('/\d+_\d+\.html',i)!=None for i in info1.loc[:,'fullURL']]
index1 = [i==False for i in index]
info1_1 = info1.loc[index,:] # 带翻页网址
info1_2 = info1.loc[index1,:] # 无翻页网址
# 将翻页网址还原
da = [re.sub('_\d+\.html','.html',str(i)) for i in info1_1.loc[:,'fullURL']]
info1_1.loc[:,'fullURL'] = da
# 翻页与非翻页网址合并
frames = [info1_1,info1_2]
info2 = pd.concat(frames)
# 或者
info2 = pd.concat([info1_1,info1_2],axis = 0) # 默认为0，即行合并
# 去重（realIP和fullURL两列相同）
info3 = info2.drop_duplicates()
# 将IP转换成字符型数据
info3.iloc[:,0] = [str(index) for index in info3.iloc[:,0]]
info3.iloc[:,1] = [str(index) for index in info3.iloc[:,1]]
print("（学号 3110）还原的翻译网址如下：")
print(info3)
len(info3)
realIP fullURL
0 2683657840 http://www.lawtime.cn/info/hunyin/hunyinfagui/...
86 838215995 http://www.lawtime.cn/info/hunyin/lhlawlhxy/20...
98 1531496412 http://www.lawtime.cn/info/hunyin/hunyinfagui/...
130 923358328 http://www.lawtime.cn/info/hunyin/zhonghun/zho...
140 1275347569 http://www.lawtime.cn/info/hunyin/lhlawlhxy/20...
... ... ...
837191 3897562894 http://www.lawtime.cn/info/hunyin/lhlawlhxy/20...
837362 3458366734 http://www.lawtime.cn/info/hunyin/jhsy/daiyun/...
837370 2526756791 http://www.lawtime.cn/info/hunyin/hynews/20101...
837376 4267065457 http://www.lawtime.cn/info/hunyin/lhlawlhxy/20...
837434 3271035001 http://www.lawtime.cn/info/hunyin/lhlawlhxy/20...
[16570 rows x 2 columns]
16570
筛选浏览次数不满两次的用户
# 代码11-12 筛选浏览次数不满两次的用户
# 筛选满足一定浏览次数的IP
IP_count = info3['realIP'].value_counts()
# 找出IP集合
IP = list(IP_count.index)
count = list(IP_count.values)
# 统计每个IP的浏览次数，并存放进IP_count数据框中,第一列为IP，第二列为浏览次数
IP_count = pd.DataFrame({'IP':IP,'count':count})
print("（学号 3110）")
print(IP_count)
# 筛选出浏览网址在n次以上的IP集合
n = 2
index = IP_count.loc[:,'count']>n
IP_index = IP_count.loc[index,'IP']
print(IP_index)
IP count
0 2609113527 895
1 3812410744 140
2 225896631 59
3 242673847 56
4 1190924814 48
... ... ...
10524 3494221838 1
10525 1219597838 1
10526 49885111 1
10527 2861434551 1
10528 2306969614 1
[10529 rows x 2 columns]
0 2609113527
1 3812410744
2 225896631
3 242673847
4 1190924814
...
865 3634500980
866 1519157623
867 3851633265
868 2213364337
869 1938534819
Name: IP, Length: 870, dtype: object
划分数据集
# 划分IP集合为训练集和测试集
index_tr = sample(range(0,len(IP_index)),int(len(IP_index)*0.8)) # 或者np.random.sample
index_te = [i for i in range(0,len(IP_index)) if i not in index_tr]
IP_tr = IP_index[index_tr]
IP_te = IP_index[index_te]
# 将对应数据集划分为训练集和测试集
index_tr = [i in list(IP_tr) for i in info3.loc[:,'realIP']]
index_te = [i in list(IP_te) for i in info3.loc[:,'realIP']]
data_tr = info3.loc[index_tr,:]
data_te = info3.loc[index_te,:]
print("（学号 3110）")
print(len(data_tr))
IP_tr = data_tr.iloc[:,0] # 训练集IP
url_tr = data_tr.iloc[:,1] # 训练集网址
IP_tr = list(set(IP_tr)) # 去重处理
url_tr = list(set(url_tr)) # 去重处理
len(url_tr)
4542
2448
标签：count,
...,
www,
电子商务,
cn,
index,
用户,
第六周,
http
From： https://www.cnblogs.com/zhilin00/p/17282493.html