非去哪儿在贷2017-10-31明细

审计第二阶段/白条（非去哪儿）/白条在贷明细20171031.py

+# coding: utf-8
+# Author: Perry_Zhu
+"""
+说明：由于白条现金支付不存在在贷所以在做白条在贷的时候可以忽略白条现金支付
+本代码用于出截止2017-09-30和截止2017-10-31的在贷
+"""
+import datetime
+import sys
+
+import pandas as pd
+import pyexcelerate as pe
+from sqlalchemy import create_engine
+
+reload(sys)
+sys.setdefaultencoding('utf8')
+engine_new_transaction = create_engine('mysql+mysqldb://internal_r:ArbNgtvlJzZHXsEu@172.16.3.201:3306/baitiao_audit?charset=utf8', echo=True)
+end_point = '2017-11-01'
+watch_point = datetime.date(2017, 10, 31)
+out_file_path = u'E:/数据汇总/白条/在贷/'
+query_loan = """
+    SELECT 
+        tt.order_no,tt.term_no,
+        min(tt.deadline)deadline,SUM(tt.principle)principle
+    FROM(
+    SELECT
+        plan.order_no,
+        plan.term_no,
+        date(plan.deadline)deadline,
+        sum(plan.principle)principle
+    FROM baitiao_order bo
+    JOIN new_transaction.user_bt_repayment_plan plan ON bo.ref_id=plan.ref_id
+    WHERE
+        bo.loan_paid_at >= '2015-01-01'
+    AND bo.loan_paid_at < '%s'
+    AND bo.is_active =1
+    and bo.baitiao_type=0
+    AND bo.funding_corp_id = 1
+    GROUP BY 1,2
+    
+    UNION ALL
+        SELECT
+            plan.order_no,
+            plan.term_no,
+            date(plan.deadline)deadline,
+            sum(plan.principle)principle
+        FROM
+            baitiao_order bo
+        JOIN new_transaction.user_bt_repayment_plan plan ON bo.ref_id=plan.ref_id and bo.baitiao_type=0
+        WHERE
+            bo.train_cancel_at >= '2015-01-01'
+        AND bo.train_cancel_at < '%s'
+        AND bo.is_active =- 2
+        AND bo.funding_corp_id = 1
+        GROUP BY 1,2
+    )tt
+    GROUP BY 1,2
+
+"""
+loan_info = """
+    SELECT tt.order_no,SUM(tt.real_loan_amount) 订单金额,max(tt.contract_term)合同期数,
+          tt.merchant_name 商户,min(tt.loan_paid_at)放款时间 
+    FROM(
+    SELECT
+        o.order_no,
+        SUM(o.real_loan_amount) real_loan_amount,
+        o.contract_term,
+        m.merchant_name,
+        date(o.loan_paid_at) loan_paid_at
+    FROM
+        baitiao_order o
+    JOIN merchant m ON o.merchantId = m.merchant_id AND o.is_active=1 AND o.funding_corp_id = 1 and o.baitiao_type=0
+    GROUP BY 1
+    
+    UNION ALL
+    
+    SELECT
+        o.order_no,
+        SUM(o.train_cancel_at) real_loan_amount,
+        o.contract_term,
+        m.merchant_name,
+        date(o.train_cancel_at) loan_paid_at
+    FROM
+        baitiao_order o
+    JOIN merchant m ON o.merchantId = m.merchant_id AND o.is_active=1 AND o.funding_corp_id = -2 and o.baitiao_type=0
+    GROUP BY 1
+    )tt
+    GROUP BY 1
+
+"""
+
+repay_online = """
+    SELECT
+        t2.order_no,
+        t2.term_no,
+        SUM(ifnull(t1.principle, 0) - IFNULL(t1.mitigate_principle, 0)) paid_principle
+    FROM
+        new_transaction.baitiao_repay_plan_repay_record_ref t1
+    JOIN new_transaction.repay_record_online t3 ON t1.repay_channel in(1,2,3,4,5,6,7,8,9,10,11,12,22,23,24) AND t3.id = t1.record_id 
+        AND t3.bill_time >= '2015-01-01' AND t3.bill_time < '%s'
+        and t1.id not in(14164355,14164357,14164359,13818155,13818157,13818159,13533131,13996965,13996971,13996967,
+                  13996957,13996959,13996961,13996963,13996973,13996975,13996977,16774675,14471253)
+    JOIN new_transaction.user_bt_repayment_plan t2 ON t2.id = t1.plan_id
+    JOIN baitiao_order t5 ON t2.ref_id = t5.ref_id AND t5.funding_corp_id =1
+    GROUP BY 1,2
+"""
+repay_ali = """
+    SELECT
+        t2.order_no,
+        t2.term_no,
+        SUM(ifnull(t1.principle,0)-ifnull(t1.mitigate_principle,0)) paid_principle
+    FROM
+        new_transaction.baitiao_repay_plan_repay_record_ref t1
+    JOIN new_transaction.user_bt_repayment_plan t2 ON t2.id = t1.plan_id
+    JOIN new_transaction.offline_alipay_record t3 ON t1.repay_channel = 13 AND t3.id = t1.record_id  AND  t3.is_ref=1 
+        AND t3.transfer_time>='2015-01-01' AND t3.transfer_time<'%s'
+    JOIN baitiao_order t5 ON t2.ref_id = t5.ref_id  AND t5.funding_corp_id = 1
+    GROUP BY 1,2
+"""
+repay_bank = """
+    SELECT
+        t2.order_no,
+        t2.term_no,
+        SUM(ifnull(t1.principle,0)-ifnull(t1.mitigate_principle,0)) paid_principle
+    FROM
+            new_transaction.baitiao_repay_plan_repay_record_ref t1
+    JOIN new_transaction.user_bt_repayment_plan t2 ON t2.id = t1.plan_id
+    JOIN new_transaction.offline_bank_repay_record t3 ON t1.repay_channel IN (14, 15, 16) AND t3.id = t1.record_id 
+        AND t3.transfer_time>='2015-01-01' AND t3.transfer_time<'%s'
+    JOIN baitiao_order t5 ON t2.ref_id = t5.ref_id 
+    GROUP BY 1,2
+"""
+
+
+df_info = pd.read_sql(loan_info, engine_new_transaction)
+df_loan_all = pd.read_sql(query_loan % (end_point, end_point), engine_new_transaction)
+df_online = pd.read_sql(repay_online % end_point, engine_new_transaction)
+df_ali = pd.read_sql(repay_ali % end_point, engine_new_transaction)
+df_bank = pd.read_sql(repay_bank % end_point, engine_new_transaction)
+df_repay = pd.concat([df_online, df_ali, df_bank], axis=0, ignore_index=True)
+df_repay.dropna(axis=0, inplace=True)
+df_loan_all.order_no = df_loan_all.order_no.astype(str)
+df_loan_all.term_no = df_loan_all.term_no.astype(int)
+df_info.order_no = df_info.order_no.astype(str)
+df_repay.order_no = df_repay.order_no.astype(str)
+df_repay.term_no = df_repay.term_no.astype(int)
+#去除无效放款
+df_loan = df_loan_all.loc[df_loan_all['principle'] > 0]
+df_repay = df_repay.groupby(['order_no', 'term_no'])['paid_principle'].agg(['sum']).reset_index().rename(columns={'sum': 'paid_principle'})
+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['flag'] = (watch_point-df_out['deadline']).dt.days
+df_out.fillna(0, inplace=True)
+df_out['zaidai'] = df_out['principle'] - df_out['paid_principle']
+#用于将在贷为负值的置0，建议使用   df_out = df_out.loc[df_out['zaidai']>0]
+df_out.loc[df_out['order_no'].isin(
+    ['trainmall1483695024282', 'trainmall1484301600672', 'trainmall1486362269554', 'trainmall1487920296689',
+     'trainmall1491536200793', 'trainmall1483695024282',
+     'trainmall1484301600672', 'trainmall1486362269554', 'grabtrainmall1484650405321', 'trainmall1495785791995',
+     'trainmall1499935282194', 'trainmall16008368458050561', 'trainmall16008408858634241',
+     'trainmall16112348780571649', 'trainmall16112552463312897']), 'zaidai'] = 0
+df_out = df_out.loc[df_out['zaidai']>0]
+df_out.loc[(df_out['flag']<=0),'0'] = df_out['zaidai']
+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']
+#在贷按order_no展开明细
+df = df_out.groupby(['order_no'])['0','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.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','0','01-30','31-60','61-90','91-120','121-150','151-180','181-360','361+']]
+data_list = df.values.tolist()
+data_list.insert(0, df.columns.tolist())
+wb = pe.Workbook()
+wb.new_sheet(u'明细', data=data_list)
+wb.save(out_file_path + u'非去哪儿白条2017-10-31在贷明细.xlsx')
+#按月汇总在贷
+df = df_out.groupby(['order_no','deadline'])['0','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',copy=False)
+df.rename(columns={'order_no': u'订单号','deadline':u'应还时间'}, inplace=True)
+df[u'放款年'] = pd.to_datetime(df[u'放款时间']).dt.year
+df[u'放款月'] = pd.to_datetime(df[u'放款时间']).dt.month
+df[u'应还年'] = df[u'应还时间'].dt.year
+df[u'应还月'] = df[u'应还时间'].dt.month
+df_result = df.groupby([u'放款年',u'放款月',u'合同期数',u'应还年',u'应还月'])['0','01-30','31-60','61-90','91-120','121-150','151-180','181-360','361+'].sum().reset_index()
+df_result.to_excel(out_file_path + u'白条在贷汇总_%s.xlsx' % end_point, index=None)
+