75/main.py
2024-10-09 03:19:56 +00:00

121 lines
3.3 KiB
Python

#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
# In[3]:
from tms_data_interface import SQLQueryInterface
seq = SQLQueryInterface(schema="transactionschema")
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seq.execute_raw("show tables")
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query = """
select final.CUSTOMER_NUMBER_main as Focal_id,
CAST(final.Total_hrc_transaction_amount AS DECIMAL(18, 2)) AS Total_hrc_transaction_amount,
final.Unique_country_codes,
final.SEGMENT,
final.RISK,
final.SAR_FLAG
from
(
(
select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,
subquery.Total_hrc_transaction_amount,
subquery.Unique_country_codes
from
(
select customer_number as CUSTOMER_NUMBER_1,
sum(transaction_amount) as Total_hrc_transaction_amount,
array_join(array_agg(DISTINCT benef_cntry_code), ',') AS unique_country_codes
from
(
select *
from {trans_data} trans_table
left join {acc_data} acc_table
on trans_table.benef_account_number = acc_table.account_number
) trans
where account_number not in ('None')
group by customer_number
) subquery
) main
left join
(
select cd.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,
cd.SEGMENT,
cd.RISK,
case
when ad.SAR_FLAG is NULL then 'N'
else ad.SAR_FLAG
end as SAR_FLAG
from
(
select customer_number as CUSTOMER_NUMBER_3,
business_segment as SEGMENT,
case
when RISK_CLASSIFICATION = 1 then 'Low Risk'
when RISK_CLASSIFICATION = 2 then 'Medium Risk'
when RISK_CLASSIFICATION = 3 then 'High Risk'
else 'Unknown Risk'
end AS RISK
from {cust_data}
) cd
left join
(
select customer_number as CUSTOMER_NUMBER_4,
sar_flag as SAR_FLAG
from {alert_data}
) ad
on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4
) as cust_alert
on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main
) as final
"""
# In[6]:
from tms_data_interface import SQLQueryInterface
class Scenario:
seq = SQLQueryInterface(schema="transactionschema")
def logic(self, **kwargs):
row_list = self.seq.execute_raw(query.format(trans_data="transaction10m",
cust_data="customer_data_v1",
acc_data="account_data_v1",
alert_data="alert_data_v1")
)
cols = ["Focal_id", "Total_hrc_transaction_amount", "Unique_country_codes",
"Segment", "Risk", "SAR_FLAG"]
df = pd.DataFrame(row_list, columns = cols)
return df
# In[7]:
# sen = Scenario()
# sen.logic()
# In[ ]: