RFM rank customers

RFM_rank.py

#split metric X into quartiles
def get_ranks(x):
   pct_75 = np.percentile(x, 75)
   pct_50 = np.percentile(x, 50)
   pct_25 = np.percentile(x, 25)
   return {'max':x.max(), '75':pct_75, '50':pct_50, '25':pct_25, 'min':x.min()}

#rank Recency metric
def rank_r(x):
    i = x.R_days_since_last_invoice
    r = 0
    if i <= R_pct['25']:
        r = 1
    if (i >= R_pct['25']) and (i < R_pct['50']):
        r = 2
    if (i >= R_pct['50']) and (i < R_pct['75']):
        r = 3
    if i >= R_pct['75']:
        r = 4
    return r

#.... F and M are ranked similarly, see GitHub for full code.