python RFM分析

import numpy

import pandas

data = pandas.read_csv(

'D:\\PDA\\5.7\\data.csv'

)

data['DealDateTime'] = pandas.to_datetime(

data.DealDateTime,

format='%Y/%m/%d'

)

data['DateDiff'] = pandas.to_datetime(

'today'

) - data['DealDateTime']

data['DateDiff'] = data['DateDiff'].dt.days

R_Agg = data.groupby(

by=['CustomerID']

)['DateDiff'].agg({

'RecencyAgg': numpy.min

})

F_Agg = data.groupby(

by=['CustomerID']

)['OrderID'].agg({

'FrequencyAgg': numpy.size

})

M_Agg = data.groupby(

by=['CustomerID']

)['Sales'].agg({

'MonetaryAgg': numpy.sum

})

aggData = R_Agg.join(F_Agg).join(M_Agg)

bins = aggData.RecencyAgg.quantile(

q=[0, 0.2, 0.4, 0.6, 0.8, 1],

interpolation='nearest'

)

bins[0] = 0

labels = [5, 4, 3, 2, 1]

R_S = pandas.cut(

aggData.RecencyAgg,

bins, labels=labels

)

bins = aggData.FrequencyAgg.quantile(

q=[0, 0.2, 0.4, 0.6, 0.8, 1],

interpolation='nearest'

)

bins[0] = 0;

labels = [1, 2, 3, 4, 5];

F_S = pandas.cut(

aggData.FrequencyAgg,

bins, labels=labels

)

bins = aggData.MonetaryAgg.quantile(

q=[0, 0.2, 0.4, 0.6, 0.8, 1],

interpolation='nearest'

)

bins[0] = 0

labels = [1, 2, 3, 4, 5]

M_S = pandas.cut(

aggData.MonetaryAgg,

bins, labels=labels

)

aggData['R_S']=R_S

aggData['F_S']=F_S

aggData['M_S']=M_S

aggData['RFM'] = 100*R_S.astype(int) + 10*F_S.astype(int) + 1*M_S.astype(int)

bins = aggData.RFM.quantile(

q=[

0, 0.125, 0.25, 0.375, 0.5,

0.625, 0.75, 0.875, 1

],

interpolation='nearest'

)

bins[0] = 0

labels = [1, 2, 3, 4, 5, 6, 7, 8]

aggData['level'] = pandas.cut(

aggData.RFM,

bins, labels=labels

)

aggData = aggData.reset_index()

fe=aggData.sort_values(

['level', 'RFM'],

ascending=[1, 1]

)

dd=aggData.groupby(

by=['level']

)['CustomerID'].agg({

'size':numpy.size

})