python怎么計(jì)算回購(gòu)率

def huigou(saleflow,former_quarter_start_end=['2019-10-01 00:00:00','2020-01-01 00:00:00'],          after_quarter_start_end=['2020-01-01 00:00:00','2020-04-01 00:00:00'] ,          groupby_list_vip =['shopid_cardid','shopid_branch','段位'],          groupby_list_branch=['shopid_branch','段位']):        #每個(gè)第一次購(gòu)買(mǎi) , 也就是 新客的流水    saleflow_first = saleflow.groupby(groupby_list_vip).oper_date.min().reset_index().rename(columns={'oper_date':'date_1st'})    # 挑選出 前面季度的新客    former_new =saleflow_first[(saleflow_first['date_1st']<pd.to_datetime(former_quarter_start_end[1]))&(saleflow_first['date_1st']>pd.to_datetime(former_quarter_start_end[0]))]    #挑選出 后一季度的流水    after_flow  =  saleflow[(saleflow['oper_date']<pd.to_datetime(after_quarter_start_end[1]))&(saleflow['oper_date']>pd.to_datetime(after_quarter_start_end[0]))]    ## join 出 前面季度會(huì)員在后一季度的流水    fugou_flow = former_new.merge(after_flow,how='right')            # 計(jì)數(shù)    former_NEW_count=saleflow_first.groupby(groupby_list_branch).shopid_cardid.nunique().reset_index()\    .rename(columns={'shopid_cardid':'shopid_cardid_new_count'})
    after_BACK_count= fugou_flow.groupby(groupby_list_branch).shopid_cardid.nunique().reset_index().\    rename(columns={'shopid_cardid':'shopid_cardid_back_count'})    df = former_NEW_count.merge(after_BACK_count,how='left')       return df

# 計(jì)算每個(gè)月的 次月回頭率 即1月回頭率import arrowmonth_list =[each.format('YYYY-MM-DD HH:mm:SS') for each in arrow.Arrow.range('month', arrow.get('2019-07-01 00:00:00','YYYY-MM-DD HH:mm:SS'),                                                                          arrow.get('2020-05-01 00:00:00','YYYY-MM-DD HH:mm:SS'))]n_space=1huigou_name= 'next_'+ str(n_space)+'_month_rebuy_rate'df_empty =pd.DataFrame(columns=('pinpai', 'shopid_cardid_new_count',                                'shopid_cardid_back_count',huigou_name, '當(dāng)前月', 'shopId'))

for i in range(len(month_list)-n_space-1):
    print(i,'當(dāng)前月：',month_list[i])    ss = huigou(yyp_vipflow ,former_quarter_start_end=[month_list[i],month_list[i+n_space]],          after_quarter_start_end= [ month_list[i+n_space] , month_list[i+n_space+1] ] ,          groupby_list_vip =['shopid_cardid','shopid_branch','pinpai'],          groupby_list_branch=['shopid_branch','pinpai'])
    new_sum = ss.groupby(['pinpai']).shopid_cardid_new_count.sum().reset_index()
    back_sum =ss.groupby(['pinpai']).shopid_cardid_back_count.sum().reset_index()
    result = new_sum.merge(back_sum)        result[huigou_name] = result['shopid_cardid_back_count'] / result['shopid_cardid_new_count']     result['當(dāng)前月'] =month_list[i][0:10]    result['shopId'] =shopId    df_empty=df_empty.append(result)