Skip to content

D
duizhang_tools
Commit 52020acf authored by data—王林芳's avatar data—王林芳
Browse files
Options

去哪儿在贷done

parent 163d69a0
Expand all Hide whitespace changes
Inline Side-by-side
Showing with 34 additions and 27 deletions
审计第二阶段/去哪儿/在贷-林芳版本/去哪儿-在贷-汇总表.ipynb
View file @ 52020acf
......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 16,
"metadata": {
"collapsed": true
},
......@@ -24,20 +24,20 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"file_path = u'E:/量化派/去哪儿/常规出账/'\n",
"file_path = u'E:/量化派/去哪儿/在贷/'\n",
"file_name = u'在贷金额_%s_%s.xlsx'\n",
"engine_qunaer = create_engine('mysql+mysqldb://internal_r:ArbNgtvlJzZHXsEu@172.16.3.201:3306/qunaer_new?charset=utf8',echo=False)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 18,
"metadata": {
"collapsed": true
},
......@@ -52,18 +52,18 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {
"collapsed": true
"collapsed": false
},
"outputs": [],
"source": [
"sql_pay='''\n",
"select product_no,stages,date(trans_time) loan_time from qunaer_new.pay_detail where fund_code =1\n",
"select product_no,stages,date(loan_time) loan_time,loan_amount from qunaer_new.pay_detail where fund_code =1\n",
"'''\n",
"df_pay=pd.read_sql(sql_pay,engine_qunaer)\n",
"sql_baoli_pay='''\n",
"select product_no,stages ,date(loan_time) loan_time from qunaer_new.baoli_pay_detail where fund_code = 1\n",
"select product_no,stages ,date(loan_time) loan_time,loan_amount from qunaer_new.baoli_pay_detail where fund_code = 1\n",
"'''\n",
"df_pay_baoli=pd.read_sql(sql_baoli_pay,engine_qunaer)\n",
"df_pay=pd.concat([df_pay,df_pay_baoli],ignore_index=True)"
......@@ -71,7 +71,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 19,
"metadata": {
"collapsed": false
},
......@@ -174,7 +174,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 20,
"metadata": {
"collapsed": true
},
......@@ -185,7 +185,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 21,
"metadata": {
"collapsed": false
},
......@@ -193,10 +193,10 @@
{
"data": {
"text/plain": [
"(63292349.239999957, 1136020154.9100001)"
"(61429343.479999997, 1134149141.5299995)"
]
},
"execution_count": 10,
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
......@@ -230,21 +230,21 @@
},
{
"cell_type": "code",
"execution_count": 270,
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#====变量定义\n",
"user_plan_2016='repayment_plan_2016'\n",
"user_plan_2017='repayment_plan_2017'\n",
"listen_time = datetime.date(2017,10,31)"
"user_plan_2016='repayment_plan_2016_201709'\n",
"user_plan_2017='repayment_plan_201709'\n",
"listen_time = datetime.date(2017,9,30)"
]
},
{
"cell_type": "code",
"execution_count": 271,
"execution_count": 23,
"metadata": {
"collapsed": true
},
......@@ -258,7 +258,7 @@
},
{
"cell_type": "code",
"execution_count": 272,
"execution_count": 24,
"metadata": {
"collapsed": true
},
......@@ -274,7 +274,7 @@
},
{
"cell_type": "code",
"execution_count": 273,
"execution_count": 25,
"metadata": {
"collapsed": true
},
......@@ -289,7 +289,7 @@
},
{
"cell_type": "code",
"execution_count": 274,
"execution_count": 26,
"metadata": {
"collapsed": false
},
......@@ -306,7 +306,7 @@
},
{
"cell_type": "code",
"execution_count": 275,
"execution_count": 27,
"metadata": {
"collapsed": false
},
......@@ -314,10 +314,10 @@
{
"data": {
"text/plain": [
"136249069.98999998"
"61429343.479999974"
]
},
"execution_count": 275,
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
......@@ -416,9 +416,16 @@
"df_plan_tmp['no_pay']=df_plan_tmp[u'zaidai'] / df_plan_tmp['p_count']\n",
"df_plan_tmp.deadline=pd.to_datetime(df_plan_tmp.deadline).dt.date\n",
"df_plan_tmp['date_diff']=df_plan_tmp.deadline.apply(lambda x:(listen_time-x).days)\n",
"df_plan_tmp.ix[df_plan_tmp['date_diff'] < 0 ,'date_name'] =u'未到期'\n",
"df_plan_tmp.ix[(df_plan_tmp['date_diff'] >= 0) & (df_plan_tmp['date_diff'] <= 30 ) ,'date_name'] = u'逾期30天'\n",
"df_plan_tmp.ix[(df_plan_tmp['date_diff'] > 30) ,'date_name'] = u'逾期30天上'\n",
"df_plan_tmp.ix[df_plan_tmp['date_diff'] <= 0 ,'date_name'] =u'未到期'\n",
"df_plan_tmp.ix[(df_plan_tmp['date_diff'] > 0) & (df_plan_tmp['date_diff'] <= 30 ) ,'date_name'] = u'逾期1-30天'\n",
"df_plan_tmp.ix[(df_plan_tmp['date_diff'] > 30) & (df_plan_tmp['date_diff'] <= 60),'date_name'] = u'逾期31-60天'\n",
"df_plan_tmp.ix[(df_plan_tmp['date_diff'] > 60) & (df_plan_tmp['date_diff'] <= 90),'date_name'] = u'逾期61-90天'\n",
"df_plan_tmp.ix[(df_plan_tmp['date_diff'] > 90) & (df_plan_tmp['date_diff'] <= 120),'date_name'] = u'逾期91-120天'\n",
"df_plan_tmp.ix[(df_plan_tmp['date_diff'] > 90) & (df_plan_tmp['date_diff'] <= 120),'date_name'] = u'逾期91-120天'\n",
"df_plan_tmp.ix[(df_plan_tmp['date_diff'] > 120) & (df_plan_tmp['date_diff'] <= 150),'date_name'] = u'逾期121-150天'\n",
"df_plan_tmp.ix[(df_plan_tmp['date_diff'] > 150) & (df_plan_tmp['date_diff'] <= 180),'date_name'] = u'逾期151-180天'\n",
"df_plan_tmp.ix[(df_plan_tmp['date_diff'] > 180) & (df_plan_tmp['date_diff'] <= 360),'date_name'] = u'逾期181-360天'\n",
"df_plan_tmp.ix[(df_plan_tmp['date_diff'] > 360),'date_name'] = u'逾期361天上'\n",
"df_plan_tmp = pd.merge(df_plan_tmp,df_pay[['product_no','loan_time','stages']],on='product_no',how='left')\n",
"df_plan_tmp.deadline=pd.to_datetime(df_plan_tmp.deadline).dt.date\n",
"df_plan_tmp.drop_duplicates(['product_no','deadline'],inplace=True)\n",
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment