# -*- coding: utf-8 -*-
# 代码10-8
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
from sklearn.externals import joblib
# 读取数据
Xtrain = pd.read_excel('../tmp/sj_final.xlsx')
ytrain = pd.read_excel('../data/water_heater_log.xlsx')
test = pd.read_excel('../data/test_data.xlsx')
# 训练集测试集区分。
x_train, x_test, y_train, y_test = Xtrain.iloc[:,5:],test.iloc[:,4:-1],\
ytrain.iloc[:,-1],test.iloc[:,-1]
# 标准化
stdScaler = StandardScaler().fit(x_train)
x_stdtrain = stdScaler.transform(x_train)
x_stdtest = stdScaler.transform(x_test)
# 建立模型
bpnn = MLPClassifier(hidden_layer_sizes = (17,10), max_iter = 200, solver = 'lbfgs',random_state=50)
bpnn.fit(x_stdtrain, y_train)
# 保存模型
joblib.dump(bpnn,'../tmp/water_heater_nnet.m')
print('构建的模型为：\n',bpnn)
# -*- coding: utf-8 -*-
# 代码11-2
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_)
# 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:[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))
# 代码11-7
# 分析网页点击次数
# 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)
# 代码11-8
# 分析浏览一次的用户行为
engine = create_engine('mysql+pymysql://root:[email protected]:3306/test?charset=utf8')
all_gzdata = pd.read_sql_table('all_gzdata', con = engine) # 读取all_gzdata数据
#对realIP进行统计
# 提取浏览1次网页的数据
real_count = pd.DataFrame(all_gzdata.groupby("realIP")["realIP"].count())
real_count.columns = ["count"]
real_count["realIP"] = real_count.index.tolist()
user_one = real_count[(real_count["count"] == 1)] # 提取只登录一次的用户
# 通过realIP与原始数据合并
real_one = pd.merge(user_one, all_gzdata, left_on="realIP", right_on="realIP")
# 统计浏览一次的网页类型
URL_count = pd.DataFrame(real_one.groupby("fullURLId")["fullURLId"].count())
URL_count.columns = ["count"]
URL_count.sort_values(by='count', ascending=False, inplace=True) # 降序排列
# 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)
# 代码11-9
# 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
# -*- coding: utf-8 -*-
# 代码11-2
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_)
# 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:[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))
# 代码11-7
# 分析网页点击次数
# 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)
# 代码11-8
# 分析浏览一次的用户行为
engine = create_engine('mysql+pymysql://root:[email protected]:3306/test?charset=utf8')
all_gzdata = pd.read_sql_table('all_gzdata', con = engine) # 读取all_gzdata数据
#对realIP进行统计
# 提取浏览1次网页的数据
real_count = pd.DataFrame(all_gzdata.groupby("realIP")["realIP"].count())
real_count.columns = ["count"]
real_count["realIP"] = real_count.index.tolist()
user_one = real_count[(real_count["count"] == 1)] # 提取只登录一次的用户
# 通过realIP与原始数据合并
real_one = pd.merge(user_one, all_gzdata, left_on="realIP", right_on="realIP")
# 统计浏览一次的网页类型
URL_count = pd.DataFrame(real_one.groupby("fullURLId")["fullURLId"].count())
URL_count.columns = ["count"]
URL_count.sort_values(by='count', ascending=False, inplace=True) # 降序排列
# 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)
# 代码11-9
# 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
# -*- coding: utf-8 -*-
# 代码11-2
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_)
# 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:[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))
# 代码11-7
# 分析网页点击次数
# 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)
# 代码11-8
# 分析浏览一次的用户行为
engine = create_engine('mysql+pymysql://root:[email protected]:3306/test?charset=utf8')
all_gzdata = pd.read_sql_table('all_gzdata', con = engine) # 读取all_gzdata数据
#对realIP进行统计
# 提取浏览1次网页的数据
real_count = pd.DataFrame(all_gzdata.groupby("realIP")["realIP"].count())
real_count.columns = ["count"]
real_count["realIP"] = real_count.index.tolist()
user_one = real_count[(real_count["count"] == 1)] # 提取只登录一次的用户
# 通过realIP与原始数据合并
real_one = pd.merge(user_one, all_gzdata, left_on="realIP", right_on="realIP")
# 统计浏览一次的网页类型
URL_count = pd.DataFrame(real_one.groupby("fullURLId")["fullURLId"].count())
URL_count.columns = ["count"]
URL_count.sort_values(by='count', ascending=False, inplace=True) # 降序排列
# 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)
# 代码11-9
# 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
# -*- coding: utf-8 -*-
# 代码11-15
import pandas as pd
# 读取保存的推荐结果
Res = pd.read_csv('./tmp/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：count,index,pd,sql,counts,type,week6 From： https://www.cnblogs.com/doushiyaoyan/p/17368948.html