python应用实例分析

#naifen_vipflow.columnsss1= naifen_vipflow[[ 'flow_no','shopid_cardid','item_name']].rename(columns={'item_name':'item_name_naifen'})ss2 = ysj_vipflow[['flow_no','item_name']].rename(columns={'item_name':'item_name_ysj'})flow_no_naifen_ysj= ss1.merge(ss2, on='flow_no')  # 同时购买益生菌和奶粉的 订单 和会员
#flow_no_naifen_ysjformer_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']#naifen_vipflow.columns#奶粉新客 #每个会员第一次购买 , 也就是 新客的流水saleflow= naifen_vipflowsaleflow_first = saleflow.groupby(groupby_list_vip).oper_date.min().reset_index().rename(columns={'oper_date':'date_1st'})# 挑选出 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]))]# 后一季度的新客 ，也就是本季度after_new =saleflow_first[(saleflow_first['date_1st']<pd.to_datetime(after_quarter_start_end[1]))&(saleflow_first['date_1st']>pd.to_datetime(after_quarter_start_end[0]))]#挑选出 后一季度的流水，也就是本季度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]))]##后一季度新客中， 同时买了奶粉和益生菌的订单号after_new_naifen_ysj= after_new.merge(flow_no_naifen_ysj)#.shopid_cardid.nunique()

def ysj_naifen_new(ysj_hsy_vipflow, naifen_vipflow,                    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']):        """    参数  ：    ysj_hsy_vipflow: 益生菌的会员流水    naifen_vipflow : 奶粉 会员流水    former_quarter_start_end：上一时间段的范围     after_quarter_start_end : 下一时间段的范围     groupby_list_vip : 会员级别的分组，包含在groupby() 中，即groupby(groupby_list_vip)    """    ss1= naifen_vipflow[[ 'flow_no','shopid_cardid','item_name']].rename(columns={'item_name':'item_name_naifen'})    ss2 = ysj_vipflow[['flow_no','item_name']].rename(columns={'item_name':'item_name_ysj'})    flow_no_naifen_ysj= ss1.merge(ss2, on='flow_no')  # 同时购买合生元益生菌和 奶粉的订单号 商品名称        saleflow= naifen_vipflow    saleflow_first = saleflow.groupby(groupby_list_vip).oper_date.min().reset_index().rename(columns={'oper_date':'date_1st'})    # 挑选出 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]))]    # 后一季度的新客    after_new =saleflow_first[(saleflow_first['date_1st']<pd.to_datetime(after_quarter_start_end[1]))&(saleflow_first['date_1st']>pd.to_datetime(after_quarter_start_end[0]))]    #挑选出 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]))]    ##后一季度新客中， 买了奶粉和益生菌的    after_new_naifen_ysj= after_new.merge(flow_no_naifen_ysj)#.shopid_cardid.nunique()        return after_new_naifen_ysj