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
})
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