电子商务网站行为分析及服务推荐

标签：engine index 电子商务 网站 推荐 pd sql counts type

# -*- coding: utf-8 -*-

# 代码11-1

import os
import pandas as pd

# # 修改工作路径到指定文件夹
# os.chdir("./")

# # 第一种连接方式
from sqlalchemy import create_engine

engine = create_engine('mysql+pymysql://root:[email protected]:3306/test?charset=utf8',echo=True)
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('../../data/0328/all_gzdata.csv', index=False, encoding='utf-8')

# -*- coding: utf-8 -*-

# 代码11-2

import pandas as pd
from sqlalchemy import create_engine

engine = create_engine('mysql+pymysql://root:123456@localhost: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_)

 

# 代码11-3

# 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)

 

# 代码11-4

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

#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))

 

# 代码11-5

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_)

# 代码11-6

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:123456@localhost: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))

# 代码11-7

# 分析网页点击次数
# 统计点击次数
engine = create_engine('mysql+pymysql://root:123456@localhost: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)

 

 

# -*- coding: utf-8 -*-

# 代码11-1

import os
import pandas as pd

# # 修改工作路径到指定文件夹
# os.chdir("./")

# # 第一种连接方式
from sqlalchemy import create_engine

engine = create_engine('mysql+pymysql://root:[email protected]:3306/test?charset=utf8',echo=True)
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('../../data/0328/all_gzdata.csv', index=False, encoding='utf-8')

# -*- coding: utf-8 -*-

# 代码11-2

import pandas as pd
from sqlalchemy import create_engine

engine = create_engine('mysql+pymysql://root:123456@localhost: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_)

 

# 代码11-3

# 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)

 

# 代码11-4

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

#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))

 

# 代码11-5

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_)

# 代码11-6

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:123456@localhost: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))

# 代码11-7

# 分析网页点击次数
# 统计点击次数
engine = create_engine('mysql+pymysql://root:123456@localhost: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)

# -*- coding: utf-8 -*-

# 代码11-1

import os
import pandas as pd

# # 修改工作路径到指定文件夹
# os.chdir("./")

# # 第一种连接方式
from sqlalchemy import create_engine

engine = create_engine('mysql+pymysql://root:[email protected]:3306/test?charset=utf8',echo=True)
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('../../data/0328/all_gzdata.csv', index=False, encoding='utf-8')

# -*- coding: utf-8 -*-

# 代码11-2

import pandas as pd
from sqlalchemy import create_engine

engine = create_engine('mysql+pymysql://root:123456@localhost: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_)

 

# 代码11-3

# 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)

 

# 代码11-4

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

#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))

 

# 代码11-5

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_)

# 代码11-6

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:123456@localhost: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))

# 代码11-7

# 分析网页点击次数
# 统计点击次数
engine = create_engine('mysql+pymysql://root:123456@localhost: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)

 

# -*- coding: utf-8 -*-

# 代码11-14

import pandas as pd
# 利用训练集数据构建模型
UI_matrix_tr = pd.DataFrame(0,index=IP_tr,columns=url_tr)
# 求用户－物品矩阵
print('start')
count = 0
for i in data_tr.index:
count += 1
if (count%1000 == 0):
print('loop1',count/1000,'k')
UI_matrix_tr.loc[data_tr.loc[i,'realIP'],data_tr.loc[i,'fullURL']] = 1
sum(UI_matrix_tr.sum(axis=1))
# 求物品相似度矩阵（因计算量较大，需要耗费的时间较久）

Item_matrix_tr = pd.DataFrame(0,index=url_tr,columns=url_tr)
print(Item_matrix_tr.index)
count = 0
for i in Item_matrix_tr.index:
for j in Item_matrix_tr.index:
count += 1
if (count%10000 == 0):
print('loop2',count/10000,'w')

a = sum(UI_matrix_tr.loc[:,[i,j]].sum(axis=1)==2)
b = sum(UI_matrix_tr.loc[:,[i,j]].sum(axis=1)!=0)
Item_matrix_tr.loc[i,j] = a/b

# 将物品相似度矩阵对角线处理为零

count = 0
for i in Item_matrix_tr.index:
count += 1
if (count%10000 == 0):
print('loop3',count/10000,'w')

Item_matrix_tr.loc[i,i]=0

# 利用测试集数据对模型评价
print(data_te)
IP_te = data_te.iloc[:,0]
url_te = data_te.iloc[:,1]
IP_te = list(set(IP_te))
url_te = list(set(url_te))

# 测试集数据用户物品矩阵
UI_matrix_te = pd.DataFrame(0,index=IP_te,columns=url_te)

count = 0
for i in data_te.index:
count += 1
if (count%10000 == 0):
print('loop4',count/10000,'w')

UI_matrix_te.loc[data_te.loc[i,'realIP'],data_te.loc[i,'fullURL']] = 1
# print(UI_matrix_te)
# 对测试集IP进行推荐
Res = pd.DataFrame('NaN',index=data_te.index,
columns=['IP','已浏览网址','推荐网址','T/F'])
Res.loc[:,'IP']=list(data_te.iloc[:,0])
Res.loc[:,'已浏览网址']=list(data_te.iloc[:,1])

print(Res)

# 开始推荐
# import numpy as np
# print(Res.loc[0,'已浏览网址'])
# print(Item_matrix_tr.loc[Res.loc[0,'已浏览网址'],:].index.shape)
# print(Item_matrix_tr.loc[Res.loc[0,'已浏览网址'],:].shape)
# print(Res.loc[:,'IP'])

for i in Res.index:
if Res.loc[i,'已浏览网址'] in list(Item_matrix_tr.index):
Res.loc[i,'推荐网址'] = Item_matrix_tr.loc[Res.loc[i,'已浏览网址'],
:].argmax()

Res.loc[i,'推荐网址'] = Item_matrix_tr.loc[Res.loc[i,'已浏览网址'],:].index[Res.loc[i,'推荐网址']]

# print(Item_matrix_tr.loc[Res.loc[i,'已浏览网址'],:].index[Res.loc[i,'推荐网址']])
# print(Item_matrix_tr.loc[Res.loc[i,'已浏览网址'],:][Res.loc[i,'推荐网址']])
# print(Res.loc[i,'推荐网址'])
if Res.loc[i,'推荐网址'] in url_te:
Res.loc[i,'T/F']=UI_matrix_te.loc[Res.loc[i,'IP'],Res.loc[i,'推荐网址']]==1
else:
Res.loc[i,'T/F'] = False
print(Res)

# 保存推荐结果
Res.to_csv('../../data/0328/Res.csv',index=False,encoding='utf8')

# -*- coding: utf-8 -*-

# 代码11-15

import pandas as pd
# 读取保存的推荐结果
Res = pd.read_csv('../../data/0328/Res.csv',keep_default_na=False, encoding='utf8')

# 计算推荐准确率
Pre = round(sum(Res.loc[:,'T/F']=='True') / (len(Res.index)-sum(Res.loc[:,'T/F']=='NaN')), 3)

print(Pre)

# 计算推荐召回率
Rec = round(sum(Res.loc[:,'T/F']=='True') / (sum(Res.loc[:,'T/F']=='True')+sum(Res.loc[:,'T/F']=='NaN')), 3)

print(Rec)

# 计算F1指标
F1 = round(2*Pre*Rec/(Pre+Rec),3)
print(F1)

 

标签：engine,index,电子商务,网站,推荐,pd,sql,counts,type
From： https://www.cnblogs.com/quanshi/p/17315340.html