generated from user_client2024/78
190 lines
5.7 KiB
Python
190 lines
5.7 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# In[1]:
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# import pandas as pd
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# query = """
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# select final.CUSTOMER_NUMBER_main as Focal_id,
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# CAST(final.Cash_deposit_total AS DECIMAL(18, 2)) AS Cash_deposit_total,
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# final.Cash_deposit_count,
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# final.SEGMENT,
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# final.RISK,
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# final.SAR_FLAG
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# from
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# (
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# (
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# select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,
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# subquery.Cash_deposit_total,
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# subquery.Cash_deposit_count
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# from
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# (
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# select customer_number as CUSTOMER_NUMBER_1,
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# sum(transaction_amount) as Cash_deposit_total,
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# count(*) as Cash_deposit_count
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# from
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# (
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# select *
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# from {trans_data} trans_table
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# left join {acc_data} acc_table
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# on trans_table.benef_account_number = acc_table.account_number
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# ) trans
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# where account_number not in ('None')
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# and transaction_desc = 'CASH RELATED TRANSACTION'
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# group by customer_number
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# ) subquery
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# ) main
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# left join
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# (
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# select cd.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,
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# cd.SEGMENT,
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# cd.RISK,
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# case
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# when ad.SAR_FLAG is NULL then 'N'
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# else ad.SAR_FLAG
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# end as SAR_FLAG
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# from
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# (
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# select customer_number as CUSTOMER_NUMBER_3,
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# business_segment as SEGMENT,
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# case
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# when RISK_CLASSIFICATION = 1 then 'Low Risk'
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# when RISK_CLASSIFICATION = 2 then 'Medium Risk'
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# when RISK_CLASSIFICATION = 3 then 'High Risk'
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# else 'Unknown Risk'
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# end AS RISK
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# from {cust_data}
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# ) cd
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# left join
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# (
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# select customer_number as CUSTOMER_NUMBER_4,
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# sar_flag as SAR_FLAG
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# from {alert_data}
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# ) ad
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# on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4
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# ) as cust_alert
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# on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main
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# ) as final
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# """
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# from tms_data_interface import SQLQueryInterface
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# class Scenario:
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# seq = SQLQueryInterface(schema="transactionschema")
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# def logic(self, **kwargs):
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# row_list = self.seq.execute_raw(query.format(trans_data="transaction10m",
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# cust_data="customer_data_v1",
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# acc_data="account_data_v1",
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# alert_data="alert_data_v1")
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# )
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# cols = ["Focal_id", "Cash_deposit_total", "Cash_deposit_count",
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# "Segment", "Risk", "SAR_FLAG"]
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# df = pd.DataFrame(row_list, columns = cols)
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# df["Cash_deposit_total"] = df["Cash_deposit_total"].astype(float)
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# return df
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# In[2]:
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import pandas as pd
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query = """
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SELECT
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t.transaction_key,
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t.transaction_date,
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t.transaction_amount,
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t.transaction_desc,
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t.benef_account_number,
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-- Account data
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a.account_number,
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a.customer_number AS acc_customer_number,
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a.account_type,
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a.branch_code,
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-- Party data
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p.customer_number AS party_customer_number,
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p.customer_name,
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p.date_of_birth,
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p.nationality,
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p.business_segment,
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CASE
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WHEN p.risk_classification = 1 THEN 'Low Risk'
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WHEN p.risk_classification = 2 THEN 'Medium Risk'
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WHEN p.risk_classification = 3 THEN 'High Risk'
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ELSE 'Unknown Risk'
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END AS risk_level,
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-- Alert data
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COALESCE(al.sar_flag, 'N') AS sar_flag
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FROM {trans_data} t
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-- Join with account data on beneficiary account
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LEFT JOIN {acc_data} a
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ON t.benef_account_number = a.account_number
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-- Join with party/customer data using account's customer number
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LEFT JOIN {cust_data} p
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ON a.customer_number = p.customer_number
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-- Join with alert data using party's customer number
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LEFT JOIN {alert_data} al
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ON p.customer_number = al.customer_number
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WHERE a.account_number IS NOT NULL
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limit 100
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"""
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from tms_data_interface import SQLQueryInterface
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class Scenario:
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seq = SQLQueryInterface(schema="transactionschema")
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def logic(self, **kwargs):
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row_list = self.seq.execute_raw(query.format(trans_data="transaction10m",
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cust_data="customer_data_v1",
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acc_data="account_data_v1",
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alert_data="alert_data_v1")
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)
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"Focal_id", "Cash_deposit_total", "Cash_deposit_count",
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"Segment", "Risk", "SAR_FLAG"
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cols = [
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"transaction_key",
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"transaction_date",
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"transaction_amount",
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"transaction_desc",
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"benef_account_number",
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"account_number",
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"acc_customer_number",
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"account_type",
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"branch_code",
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"Focal_id",
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"customer_name",
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"date_of_birth",
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"nationality",
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"Segment",
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"Risk",
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"SAR_FLAG"
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]
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df = pd.DataFrame(row_list, columns = cols)
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return df
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# In[4]:
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# sen = Scenario()
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# sen.logic()
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# In[ ]:
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