RFM客户价值分类

 # 自定义好的包,亲测可用 原数据和代码思想来自以下网址
 # https://github.com/joaolcorreia/RFM-analysis
 import datetime as dt
 import pandas as pd
 def RFM(data_,ri_qi,user_id,money,*time_):
     '''
     return:
         dataframe表格
         列有r,f,m,r_四分位分数,f_四分位分数,m_四分位分数,RFM,用户分类
         行为用户id,唯一,分类汇总后
     desc:
         如 : RFM(orders,'order_date','customer','grand_total',2018,1,2)
         返回df数据会复制到粘贴板
     params:
         data_ : 表格数据 pandas dataframe (表格中的字段名)
         ri_qi : 订单日期  (表格中的字段名)
         user_id : 用户id  (表格中的字段名)
         money : 消费金额  (表格中的字段名)
        *time_ : 不定长参数  不写默认当天

     '''

     # 设置参考时间点,无参数则默认为当天
     if time_ == ():
         NOW = dt.datetime.now()
     else:
         y,m,d = time_
         NOW = dt.datetime(y,m,d)

     # 日期列进行格式转换
     data_[ri_qi] = pd.to_datetime(data_[ri_qi])

     # 创建RFM标签
     rfmTable = data_.groupby(user_id).agg({ri_qi: lambda x: (NOW - x.max()).days, # Recency 顾客最近一次购买距今天数(days)
                                             'order_id': lambda x: len(x),      # Frequency 按顾客分组后次数,即顾客订单频次
                                             money: lambda x: x.sum()}) # Monetary Value 顾客消费金额总和
     # 日期列转换数据类型
     rfmTable[ri_qi] = rfmTable[ri_qi].astype(int)
     # 列名重命名
     rfmTable.rename(columns={ri_qi: 'recency',
                              'order_id': 'frequency',
                              money: 'monetary_value'}, inplace=True)

     # 确定RFM四分位数
     quantiles = rfmTable.quantile(q=[0.25,0.5,0.75])
     quantiles = quantiles.to_dict()

     # 创建RFM分割表
     rfmSegmentation = rfmTable.copy(deep=True)
     # Arguments (x = value, p = recency, monetary_value, frequency, k = quartiles dict)
     # 日期间隔越小越好
     def RClass(x,p,d):
         # p,d = args = ('recency',quantiles,)
         # x即rfmSegmentation['recency'].apply(RClass) 日期间隔数据
         if x <= d[p][0.25]:
             return 1
         elif x <= d[p][0.50]:
             return 2
         elif x <= d[p][0.75]:
             return 3
         else:
             return 4

     # Arguments (x = value, p = recency, monetary_value, frequency, k = quartiles dict)
     # 消费频次和金额越大越好
     # 分数范围1-4
     def FMClass(x,p,d):
         if x <= d[p][0.25]:
             return 4
         elif x <= d[p][0.50]:
             return 3
         elif x <= d[p][0.75]:
             return 2
         else:
             return 1

     # 评分应用 R F M
     rfmSegmentation['R_Quartile'] = rfmSegmentation['recency'].apply(RClass, args=('recency',quantiles,))
     rfmSegmentation['F_Quartile'] = rfmSegmentation['frequency'].apply(FMClass, args=('frequency',quantiles,))
     rfmSegmentation['M_Quartile'] = rfmSegmentation['monetary_value'].apply(FMClass, args=('monetary_value',quantiles,))

     # 评分应用 RFMClass
     rfmSegmentation['RFMClass'] = rfmSegmentation.R_Quartile.map(str) \
                             + rfmSegmentation.F_Quartile.map(str) \
                             + rfmSegmentation.M_Quartile.map(str)

     def type_(x,R,F,M):
         '''
         r,f,m即每行的r,f,m分值
         '''
         r,f,m = x[R],x[F],x[M]
         if r > 2 and f > 2 and m > 2:
             return '高价值客户'
         elif r <= 2 and f > 2 and m > 2:
             return '高重点保护客户'
         elif r > 2 and f <= 2 and m > 2:
             return '重点发展客户'
         elif r <= 2 and f <= 2 and m > 2:
             return '重点挽留客户'
         elif r > 2 and f > 2 and m <= 2:
             return '一般价值客户'
         elif r <= 2 and f > 2 and m <= 2:
             return '一般保持客户'
         elif r > 2 and f <= 2 and m <= 2:
             return '一般发展客户'
         elif r <= 2 and f <= 2 and m <= 2:
             return '潜在客户'
         else:
             return 

     rfmSegmentation['type_'] = rfmSegmentation.apply(type_,args=('R_Quartile','F_Quartile','M_Quartile'),axis=1)
     rfmSegmentation.to_clipboard() # 数据复制到  粘贴板
     return rfmSegmentation

调用函数处理后: