generated from user_client2024/78
188 lines
5.6 KiB
Python
188 lines
5.6 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# In[3]:
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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[6]:
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# import pandas as pd
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# query = """
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# SELECT
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# t.transaction_id,
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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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# cols = [
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# "transaction_id",
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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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# "party_customer_number",
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# "customer_name",
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# "date_of_birth",
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# "nationality",
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# "business_segment",
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# "risk_level",
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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[5]:
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# sen = Scenario()
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# sen.logic()
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# In[ ]:
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