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.ipynb_checkpoints/main-checkpoint.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"query = \"\"\"\n",
" select final.CUSTOMER_NUMBER_main as Focal_id,\n",
" CAST(final.Cash_deposit_total AS DECIMAL(18, 2)) AS Cash_deposit_total,\n",
" final.Cash_deposit_count,\n",
" final.SEGMENT,\n",
" final.RISK,\n",
" final.SAR_FLAG\n",
"from \n",
"(\n",
" (\n",
" select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,\n",
" subquery.Cash_deposit_total,\n",
" subquery.Cash_deposit_count\n",
" from \n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_1, \n",
" sum(transaction_amount) as Cash_deposit_total, \n",
" count(*) as Cash_deposit_count\n",
" from \n",
" (\n",
" select * \n",
" from {trans_data} trans_table \n",
" left join {acc_data} acc_table\n",
" on trans_table.benef_account_number = acc_table.account_number\n",
" ) trans\n",
" where account_number not in ('None')\n",
" and transaction_desc = 'CASH RELATED TRANSACTION'\n",
" group by customer_number\n",
" ) subquery\n",
" ) main \n",
" left join \n",
" (\n",
" select cd.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n",
" cd.SEGMENT,\n",
" cd.RISK,\n",
" case\n",
" when ad.SAR_FLAG is NULL then 'N'\n",
" else ad.SAR_FLAG\n",
" end as SAR_FLAG \n",
" from\n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_3, \n",
" business_segment as SEGMENT,\n",
" case\n",
" when RISK_CLASSIFICATION = 1 then 'Low Risk'\n",
" when RISK_CLASSIFICATION = 2 then 'Medium Risk'\n",
" when RISK_CLASSIFICATION = 3 then 'High Risk'\n",
" else 'Unknown Risk'\n",
" end AS RISK\n",
" from {cust_data}\n",
" ) cd \n",
" left join\n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_4, \n",
" sar_flag as SAR_FLAG\n",
" from {alert_data}\n",
" ) ad \n",
" on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4\n",
" ) as cust_alert\n",
" on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
") as final\n",
"\"\"\"\n",
"\n",
"from tms_data_interface import SQLQueryInterface\n",
"\n",
"class Scenario:\n",
" seq = SQLQueryInterface(schema=\"transactionschema\")\n",
"\n",
" def logic(self, **kwargs):\n",
" row_list = self.seq.execute_raw(query.format(trans_data=\"transaction10m\",\n",
" cust_data=\"customer_data_v1\",\n",
" acc_data=\"account_data_v1\",\n",
" alert_data=\"alert_data_v1\")\n",
" )\n",
" cols = [\"Focal_id\", \"Cash_deposit_total\", \"Cash_deposit_count\",\n",
" \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
" df = pd.DataFrame(row_list, columns = cols)\n",
" df[\"Cash_deposit_total\"] = df[\"Cash_deposit_total\"].astype(float)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b6c85de2-6a47-4109-8885-c138c289ec25",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# import pandas as pd\n",
"\n",
"# query = \"\"\"\n",
"# SELECT \n",
"# t.transaction_id,\n",
"# t.transaction_date,\n",
"# t.transaction_amount,\n",
"# t.transaction_desc,\n",
"# t.benef_account_number,\n",
"\n",
"# -- Account data\n",
"# a.account_number,\n",
"# a.customer_number AS acc_customer_number,\n",
"# a.account_type,\n",
"# a.branch_code,\n",
"\n",
"# -- Party data\n",
"# p.customer_number AS party_customer_number,\n",
"# p.customer_name,\n",
"# p.date_of_birth,\n",
"# p.nationality,\n",
"# p.business_segment,\n",
"# CASE\n",
"# WHEN p.risk_classification = 1 THEN 'Low Risk'\n",
"# WHEN p.risk_classification = 2 THEN 'Medium Risk'\n",
"# WHEN p.risk_classification = 3 THEN 'High Risk'\n",
"# ELSE 'Unknown Risk'\n",
"# END AS risk_level,\n",
"\n",
"# -- Alert data\n",
"# COALESCE(al.sar_flag, 'N') AS sar_flag\n",
"\n",
"# FROM {trans_data} t\n",
"\n",
"# -- Join with account data on beneficiary account\n",
"# LEFT JOIN {acc_data} a\n",
"# ON t.benef_account_number = a.account_number\n",
"\n",
"# -- Join with party/customer data using account's customer number\n",
"# LEFT JOIN {cust_data} p\n",
"# ON a.customer_number = p.customer_number\n",
"\n",
"# -- Join with alert data using party's customer number\n",
"# LEFT JOIN {alert_data} al\n",
"# ON p.customer_number = al.customer_number\n",
"\n",
"# WHERE a.account_number IS NOT NULL\n",
"# limit 100\n",
"# \"\"\"\n",
"\n",
"# from tms_data_interface import SQLQueryInterface\n",
"\n",
"# class Scenario:\n",
"# seq = SQLQueryInterface(schema=\"transactionschema\")\n",
"\n",
"# def logic(self, **kwargs):\n",
"# row_list = self.seq.execute_raw(query.format(trans_data=\"transaction10m\",\n",
"# cust_data=\"customer_data_v1\",\n",
"# acc_data=\"account_data_v1\",\n",
"# alert_data=\"alert_data_v1\")\n",
"# )\n",
"# cols = [\n",
"# \"transaction_id\",\n",
"# \"transaction_date\",\n",
"# \"transaction_amount\",\n",
"# \"transaction_desc\",\n",
"# \"benef_account_number\",\n",
"# \"account_number\",\n",
"# \"acc_customer_number\",\n",
"# \"account_type\",\n",
"# \"branch_code\",\n",
"# \"party_customer_number\",\n",
"# \"customer_name\",\n",
"# \"date_of_birth\",\n",
"# \"nationality\",\n",
"# \"business_segment\",\n",
"# \"risk_level\",\n",
"# \"sar_flag\"\n",
"# ]\n",
"# df = pd.DataFrame(row_list, columns = cols)\n",
"# return df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1f20337b-8116-47e5-8743-1ba41e2df819",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# sen = Scenario()\n",
"# sen.logic()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6de62b37-00d1-4c88-b27b-9a70e05add91",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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main.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"query = \"\"\"\n",
" select final.CUSTOMER_NUMBER_main as Focal_id,\n",
" CAST(final.Cash_deposit_total AS DECIMAL(18, 2)) AS Cash_deposit_total,\n",
" final.Cash_deposit_count,\n",
" final.SEGMENT,\n",
" final.RISK,\n",
" final.SAR_FLAG\n",
"from \n",
"(\n",
" (\n",
" select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,\n",
" subquery.Cash_deposit_total,\n",
" subquery.Cash_deposit_count\n",
" from \n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_1, \n",
" sum(transaction_amount) as Cash_deposit_total, \n",
" count(*) as Cash_deposit_count\n",
" from \n",
" (\n",
" select * \n",
" from {trans_data} trans_table \n",
" left join {acc_data} acc_table\n",
" on trans_table.benef_account_number = acc_table.account_number\n",
" ) trans\n",
" where account_number not in ('None')\n",
" and transaction_desc = 'CASH RELATED TRANSACTION'\n",
" group by customer_number\n",
" ) subquery\n",
" ) main \n",
" left join \n",
" (\n",
" select cd.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n",
" cd.SEGMENT,\n",
" cd.RISK,\n",
" case\n",
" when ad.SAR_FLAG is NULL then 'N'\n",
" else ad.SAR_FLAG\n",
" end as SAR_FLAG \n",
" from\n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_3, \n",
" business_segment as SEGMENT,\n",
" case\n",
" when RISK_CLASSIFICATION = 1 then 'Low Risk'\n",
" when RISK_CLASSIFICATION = 2 then 'Medium Risk'\n",
" when RISK_CLASSIFICATION = 3 then 'High Risk'\n",
" else 'Unknown Risk'\n",
" end AS RISK\n",
" from {cust_data}\n",
" ) cd \n",
" left join\n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_4, \n",
" sar_flag as SAR_FLAG\n",
" from {alert_data}\n",
" ) ad \n",
" on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4\n",
" ) as cust_alert\n",
" on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
") as final\n",
"\"\"\"\n",
"\n",
"from tms_data_interface import SQLQueryInterface\n",
"\n",
"class Scenario:\n",
" seq = SQLQueryInterface(schema=\"transactionschema\")\n",
"\n",
" def logic(self, **kwargs):\n",
" row_list = self.seq.execute_raw(query.format(trans_data=\"transaction10m\",\n",
" cust_data=\"customer_data_v1\",\n",
" acc_data=\"account_data_v1\",\n",
" alert_data=\"alert_data_v1\")\n",
" )\n",
" cols = [\"Focal_id\", \"Cash_deposit_total\", \"Cash_deposit_count\",\n",
" \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
" df = pd.DataFrame(row_list, columns = cols)\n",
" df[\"Cash_deposit_total\"] = df[\"Cash_deposit_total\"].astype(float)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b6c85de2-6a47-4109-8885-c138c289ec25",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# import pandas as pd\n",
"\n",
"# query = \"\"\"\n",
"# SELECT \n",
"# t.transaction_id,\n",
"# t.transaction_date,\n",
"# t.transaction_amount,\n",
"# t.transaction_desc,\n",
"# t.benef_account_number,\n",
"\n",
"# -- Account data\n",
"# a.account_number,\n",
"# a.customer_number AS acc_customer_number,\n",
"# a.account_type,\n",
"# a.branch_code,\n",
"\n",
"# -- Party data\n",
"# p.customer_number AS party_customer_number,\n",
"# p.customer_name,\n",
"# p.date_of_birth,\n",
"# p.nationality,\n",
"# p.business_segment,\n",
"# CASE\n",
"# WHEN p.risk_classification = 1 THEN 'Low Risk'\n",
"# WHEN p.risk_classification = 2 THEN 'Medium Risk'\n",
"# WHEN p.risk_classification = 3 THEN 'High Risk'\n",
"# ELSE 'Unknown Risk'\n",
"# END AS risk_level,\n",
"\n",
"# -- Alert data\n",
"# COALESCE(al.sar_flag, 'N') AS sar_flag\n",
"\n",
"# FROM {trans_data} t\n",
"\n",
"# -- Join with account data on beneficiary account\n",
"# LEFT JOIN {acc_data} a\n",
"# ON t.benef_account_number = a.account_number\n",
"\n",
"# -- Join with party/customer data using account's customer number\n",
"# LEFT JOIN {cust_data} p\n",
"# ON a.customer_number = p.customer_number\n",
"\n",
"# -- Join with alert data using party's customer number\n",
"# LEFT JOIN {alert_data} al\n",
"# ON p.customer_number = al.customer_number\n",
"\n",
"# WHERE a.account_number IS NOT NULL\n",
"# limit 100\n",
"# \"\"\"\n",
"\n",
"# from tms_data_interface import SQLQueryInterface\n",
"\n",
"# class Scenario:\n",
"# seq = SQLQueryInterface(schema=\"transactionschema\")\n",
"\n",
"# def logic(self, **kwargs):\n",
"# row_list = self.seq.execute_raw(query.format(trans_data=\"transaction10m\",\n",
"# cust_data=\"customer_data_v1\",\n",
"# acc_data=\"account_data_v1\",\n",
"# alert_data=\"alert_data_v1\")\n",
"# )\n",
"# cols = [\n",
"# \"transaction_id\",\n",
"# \"transaction_date\",\n",
"# \"transaction_amount\",\n",
"# \"transaction_desc\",\n",
"# \"benef_account_number\",\n",
"# \"account_number\",\n",
"# \"acc_customer_number\",\n",
"# \"account_type\",\n",
"# \"branch_code\",\n",
"# \"party_customer_number\",\n",
"# \"customer_name\",\n",
"# \"date_of_birth\",\n",
"# \"nationality\",\n",
"# \"business_segment\",\n",
"# \"risk_level\",\n",
"# \"sar_flag\"\n",
"# ]\n",
"# df = pd.DataFrame(row_list, columns = cols)\n",
"# return df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1f20337b-8116-47e5-8743-1ba41e2df819",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# sen = Scenario()\n",
"# sen.logic()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6de62b37-00d1-4c88-b27b-9a70e05add91",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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#!/usr/bin/env python
# coding: utf-8
# In[3]:
import pandas as pd
query = """
select final.CUSTOMER_NUMBER_main as Focal_id,
CAST(final.Cash_deposit_total AS DECIMAL(18, 2)) AS Cash_deposit_total,
final.Cash_deposit_count,
final.SEGMENT,
final.RISK,
final.SAR_FLAG
from
(
(
select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,
subquery.Cash_deposit_total,
subquery.Cash_deposit_count
from
(
select customer_number as CUSTOMER_NUMBER_1,
sum(transaction_amount) as Cash_deposit_total,
count(*) as Cash_deposit_count
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')
and transaction_desc = 'CASH RELATED TRANSACTION'
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
"""
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", "Cash_deposit_total", "Cash_deposit_count",
"Segment", "Risk", "SAR_FLAG"]
df = pd.DataFrame(row_list, columns = cols)
df["Cash_deposit_total"] = df["Cash_deposit_total"].astype(float)
return df
# In[6]:
# import pandas as pd
# query = """
# SELECT
# t.transaction_id,
# t.transaction_date,
# t.transaction_amount,
# t.transaction_desc,
# t.benef_account_number,
# -- Account data
# a.account_number,
# a.customer_number AS acc_customer_number,
# a.account_type,
# a.branch_code,
# -- Party data
# p.customer_number AS party_customer_number,
# p.customer_name,
# p.date_of_birth,
# p.nationality,
# p.business_segment,
# CASE
# WHEN p.risk_classification = 1 THEN 'Low Risk'
# WHEN p.risk_classification = 2 THEN 'Medium Risk'
# WHEN p.risk_classification = 3 THEN 'High Risk'
# ELSE 'Unknown Risk'
# END AS risk_level,
# -- Alert data
# COALESCE(al.sar_flag, 'N') AS sar_flag
# FROM {trans_data} t
# -- Join with account data on beneficiary account
# LEFT JOIN {acc_data} a
# ON t.benef_account_number = a.account_number
# -- Join with party/customer data using account's customer number
# LEFT JOIN {cust_data} p
# ON a.customer_number = p.customer_number
# -- Join with alert data using party's customer number
# LEFT JOIN {alert_data} al
# ON p.customer_number = al.customer_number
# WHERE a.account_number IS NOT NULL
# limit 100
# """
# 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 = [
# "transaction_id",
# "transaction_date",
# "transaction_amount",
# "transaction_desc",
# "benef_account_number",
# "account_number",
# "acc_customer_number",
# "account_type",
# "branch_code",
# "party_customer_number",
# "customer_name",
# "date_of_birth",
# "nationality",
# "business_segment",
# "risk_level",
# "sar_flag"
# ]
# df = pd.DataFrame(row_list, columns = cols)
# return df
# In[5]:
# sen = Scenario()
# sen.logic()
# In[ ]: