细分在贷时间点

审计第二阶段/白条（非去哪儿）/白条在贷明细--按order_no维度.py

@@ -200,7 +200,6 @@ select tt.order_no,tt.term_no,tt.paid_principle from(
     GROUP BY 1,2
 )tt
 GROUP BY 1,2
-
 """
 
 df_info = pd.read_sql(loan_info, engine_new_transaction)
@@ -221,8 +220,11 @@ df_repay = df_repay.groupby(['order_no', 'term_no'])['paid_principle'].agg({'sum
 df_out = pd.merge(df_loan, df_repay, on=['order_no', 'term_no'], how='left')
 df_out['deadline'] = pd.to_datetime(df_out.deadline)
 df_out.fillna(0, inplace=True)
-df_out['zaidai'] = df_out['principle'] - df_out['paid_principle']
 
+df_out['flag'] = (watch_point-df_out['deadline']).dt.days
+df_out=df_out[df_out['flag']>0]
+df_out=df_out[df_out['zaidai']>0]
+df_out['zaidai'] = df_out['principle'] - df_out['paid_principle']
 df_out.loc[df_out['order_no'].isin(
     ['trainmall1483695024282', 'trainmall1484301600672', 'trainmall1486362269554', 'trainmall1487920296689',
      'trainmall1491536200793', 'trainmall1483695024282',
@@ -230,13 +232,25 @@ df_out.loc[df_out['order_no'].isin(
      'trainmall1499935282194', 'trainmall16008368458050561', 'trainmall16008408858634241',
      'trainmall16112348780571649', 'trainmall16112552463312897']), 'zaidai'] = 0
 
+df_out.loc[(df_out['flag']>=1) &(df_out['flag']<=30),'01-30'] = df_out['zaidai']
+df_out.loc[(df_out['flag']>=31) &(df_out['flag']<=60),'31-60'] = df_out['zaidai']
+df_out.loc[(df_out['flag']>=61) &(df_out['flag']<=90),'61-90'] = df_out['zaidai']
+df_out.loc[(df_out['flag']>=91) &(df_out['flag']<=120),'91-120'] = df_out['zaidai']
+df_out.loc[(df_out['flag']>=121) &(df_out['flag']<=150),'121-150'] = df_out['zaidai']
+df_out.loc[(df_out['flag']>=151) &(df_out['flag']<=180),'151-180'] = df_out['zaidai']
+df_out.loc[(df_out['flag']>=181) &(df_out['flag']<=360),'181-360'] = df_out['zaidai']
+df_out.loc[(df_out['flag']>=361),'361+'] = df_out['zaidai']
+
 # df_out['zaidai'] = df_out['zaidai'].apply(lambda x: np.round(x, 2))
-df = df_out.groupby(['order_no'])['zaidai'].agg({'sum'}).reset_index().rename(columns={'sum': u'未还本金'})
+df = df_out.groupby(['order_no'])['01-30','31-60','61-90','91-120','121-150','151-180','181-360','361+'].sum().reset_index()
+df.fillna(0,inplace=True)
 df_loan.order_no = df_loan.order_no.astype(str)
 df = pd.merge(df, df_info, on='order_no', how='left')
-df = df[[u'商户', 'order_no', u'订单金额', u'合同期数', u'放款时间', u'未还本金']]
-df[u'未还本金'].fillna(0, inplace=True)
-df.rename(columns={'order_no': u'订单号'}, inplace=True)
+
+df.rename(columns={'order_no': u'订单号'
+                   }, inplace=True)
+
+df = df[[u'商户', u'订单号', u'订单金额', u'合同期数', u'放款时间','01-30','31-60','61-90','91-120','121-150','151-180','181-360','361+']]
 
 all_wb = pyexcelerate.Workbook()
 for i in xrange(0, len(df), max_limit):