#coding:utf-8
import datetime
import pandas as pd
def RFM():
trad_flow = pd.read_csv(r'../input/RFM_TRAD_FLOW.csv',encoding="GBK")
trad_flow_new = trad_flow.copy()
##trad_flow_new['time_new']=trad_flow_new['time'].apply(lambda x :timeFormat(x))
trad_flow_new['time_format'] = trad_flow_new.time.apply(timeFormat)
##pd.set_option('display.max_rows', 9)
pd.set_option('precision', 2) #小数点保留位
pd.set_option('display.max_columns', 18) #最大列数
pd.set_option('expand_frame_repr', False) #不换行显示
pd.set_option('display.width', 200)#横向最多显示的字符数
##print(trad_flow_new.head(10))
##对用户按照列cumid、type分组,列transID统计数量
F = trad_flow_new.groupby(['cumid','type'])[['transID']].count()
##print(F.head())
##对DataFrame F按照type、transID进行pivot，即行列转换
F_trans = pd.pivot_table(F, index='cumid', columns='type', values='transID')
##print(F_trans.head())
# 计算不包含Normal的数据
## 查看returned_goods、Normal 列是否有空值,如果有则用0代替。shape[0]是行数，shape[1]是列数
##print(F_trans[F_trans.returned_goods.isnull()].shape[0])#shape[1]
## 仅仅对数据列里有NULL的用0来填充
##F_trans=F_trans[F_trans.isnull().T.any()].fillna(0)
F_trans = F_trans.fillna(0)
##F_trans['Special_offer'] = F_trans['Special_offer'].fillna(0) ##单独对列FillNa
## 计算兴趣比,这里是 特价/(特价+正常)
F_trans['interest'] = F_trans.Special_offer/(F_trans.Special_offer+F_trans.Normal)
##print(F_trans.head())
#print(trad_flow_new[trad_flow_new['cumid']==19021]) #查看cumid等于19021的明细
##顾客价值信息取向
M = trad_flow_new.groupby(['cumid','type'])[['amount']].sum()
M_trans = pd.pivot_table(M,index='cumid',columns='type',values='amount')
M_trans = M_trans.fillna(0)
M_trans['value']=M_trans.Normal + M_trans.Special_offer+M_trans.returned_goods
##print(M_trans.head(10))
trad_flow_new['time_new']=trad_flow_new.time.apply(to_time)
R = trad_flow_new.groupby(['cumid'])[['time_new']].max()
##print(R.head())
from sklearn import preprocessing
#这里是等深分箱q即quantile,这里参数是分成两箱。
threshold = pd.qcut(F_trans['interest'], 2, retbins=True)[1][1]
print("F 值二分类右边界:\t"+str(threshold))
#对F dataframe做二分类转换
binarizer = preprocessing.Binarizer(threshold=threshold) ## 定义二分类转换器
single_row=F_trans['interest'].values.reshape(-1,1) ## 提取interest列到ndarray中，这里-1是指行数未知
b_f_interest = pd.DataFrame(binarizer.transform(single_row)) ##通过二分类转换器转换单列并生成DataFrame
b_f_interest.index = F_trans.index
b_f_interest.columns=['interest']
#对M dataframe的二分类转换
threshold = pd.qcut(M_trans['value'], 2, retbins=True)[1][1]
print("M 值二分类右边界:\t" + str(threshold))
binarizer = preprocessing.Binarizer(threshold=threshold)
single_row = M_trans['value'].values.reshape(-1,1)
b_m_value = pd.DataFrame(binarizer.transform(single_row))
b_m_value.index = M_trans.index
b_m_value.columns=['value']
##print(b_m_value.head())
#对R dataframe的二分类转换
threshold = pd.qcut(R['time_new'],2,retbins=True)[1][1]
print("R 值二分类右边界:\t" + str(threshold))
binarizer = preprocessing.Binarizer(threshold=threshold)
single_row = R.time_new.values.reshape(-1,1)
b_r_time= pd.DataFrame(binarizer.transform(single_row))
b_r_time.index = R.index
b_r_time.columns=['time']
total = pd.concat([b_f_interest,b_m_value,b_r_time],axis=1)
##print(total.head())
##定义标签，按照FMR 2*2*2 = 8种情况定义出客户标签
label = {
(0, 0, 0): '无兴趣-低价值-沉默',
(1, 0, 0): '有兴趣-低价值-沉默',
(1, 0, 1): '有兴趣-低价值-活跃',
(0, 0, 1): '无兴趣-低价值-活跃',
(0, 1, 0): '无兴趣-高价值-沉默',
(1, 1, 0): '有兴趣-高价值-沉默',
(1, 1, 1): '有兴趣-高价值-活跃',
(0, 1, 1): '无兴趣-高价值-活跃'
}
total['label'] = total[['interest','value','time']].apply(lambda x:label[((x[0],x[1],x[2]))], axis = 1)
print(total.head())
def timeFormat(Str):
import datetime
return datetime.datetime.strptime(Str,'%d%b%y:%H:%M:%S')
def to_time(t):
import time
out_t=time.mktime(time.strptime(t, '%d%b%y:%H:%M:%S')) ##转换为数字类型(即与1970-1-1 8:00:00的秒数差值)方便后面qcut分箱
return out_t
def boxsplit():
F_x = pd.DataFrame(data={'age': [18, 19, 23, 25, 27, 29,34,45]})
''' Method 1 等宽分箱,宽度 w= (Max-Min)/N 即 (45-18)/3= 9，则每个分箱的右边界为 Min+(N-1)*w
第一个分箱的边界是 18+9*1=27、第二个分箱的边界是 18+9*2=36、第三个分箱的边界是 18+9*3=45
'''
##print(pd.cut(F_x['age'], 3))
print(pd.cut(F_x['age'], 3).value_counts())
''' Method 2 等深分箱(等频分箱),先通过分位数计算各个分位数对应的临界值，再"等"分数据
'''
print(F_x.age.quantile([0, 1 / 3, 2 / 3, 1]))
#print(pd.qcut(F_x['age'], 3, retbins=True))
print(pd.qcut(F_x['age'], 3).value_counts())
'''通过观察不难发现等深分箱每组的数量基本一致 而等宽分箱每组的数量可能差距较大'''
'''卡方合并法(待补充)'''
def baseTime():
import time
t = (1970, 1, 1, 8, 0, 0, 3, 1, 0)
secs = time.mktime(t)
print("time.mktime(t) : %f" % secs)
print("asctime(localtime(secs)): %s" % time.asctime(time.localtime(secs)))
if __name__ == '__main__':
#print(timeFormat('14MAY10:13:31:23'))
RFM()
#a="14JUN09:17:58:34"
#print(to_time(a))
boxsplit()
baseTime()
