generated from dhairya/scenario_template
448 lines
16 KiB
Plaintext
448 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "2f9a4ca7-c066-4d93-9957-0d9145f9265d",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from tms_data_interface import SQLQueryInterface\n",
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"seq = SQLQueryInterface(schema=\"transactionschema\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "fc212ace-ca7a-45f2-8137-f436c6123652",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[['account_data_v1'],\n",
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" ['account_data_v2'],\n",
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" ['alert_data_v1'],\n",
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" ['alert_data_v2'],\n",
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" ['customer_data_v1'],\n",
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" ['customer_data_v2'],\n",
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" ['transaction10m'],\n",
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" ['transaction60m']]"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"seq.execute_raw(\"show tables\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "134d0b3d-5481-4975-af07-c80ab09d6dd2",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"query = \"\"\"\n",
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" select final.CUSTOMER_NUMBER_main as Focal_id,\n",
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" final.Credit_transaction_amount,\n",
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" final.Total_no_of_credit_transactions,\n",
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" final.Debit_transaction_amount,\n",
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" final.Total_no_of_debit_transactions,\n",
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" final.Wash_Ratio,\n",
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" final.SEGMENT,\n",
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" final.RISK,\n",
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" final.SAR_FLAG\n",
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" from \n",
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" (\n",
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" (\n",
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" select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,\n",
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" subquery.Credit_transaction_amount,\n",
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" subquery.Total_no_of_credit_transactions,\n",
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" case\n",
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" when subquery.Debit_transaction_amount is NULL then 0\n",
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" else Debit_transaction_amount\n",
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" end as Debit_transaction_amount,\n",
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" case\n",
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" when subquery.Total_no_of_debit_transactions is NULL then 0\n",
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" else Total_no_of_debit_transactions\n",
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" end as Total_no_of_debit_transactions,\n",
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" case\n",
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" when subquery.Debit_transaction_amount = 0\n",
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" or subquery.Debit_transaction_amount is NULL then 0\n",
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" else subquery.Credit_transaction_amount / subquery.Debit_transaction_amount\n",
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" end as Wash_Ratio\n",
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" from \n",
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" (\n",
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" (\n",
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" select customer_number as CUSTOMER_NUMBER_1, \n",
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" sum(transaction_amount) as Credit_transaction_amount, \n",
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" count(*) as Total_no_of_credit_transactions\n",
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" from \n",
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" (\n",
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" select * \n",
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" from {trans_data} as trans_table left join {acc_data} as acc_table\n",
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" on trans_table.benef_account_number = acc_table.account_number\n",
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" )\n",
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" where account_number not in ('None')\n",
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" group by 1\n",
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" ) credit left join\n",
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" (\n",
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" select customer_number as CUSTOMER_NUMBER_2, \n",
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" sum(transaction_amount) as Debit_transaction_amount, \n",
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" count(*) as Total_no_of_debit_transactions\n",
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" from \n",
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" (\n",
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" select * \n",
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" from {trans_data} as trans_table left join {acc_data} as acc_table\n",
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" on trans_table.orig_account_number = acc_table.account_number\n",
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" )\n",
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" where account_number not in ('None')\n",
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" group by 1\n",
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" ) debit on credit.CUSTOMER_NUMBER_1 = debit.CUSTOMER_NUMBER_2 \n",
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" ) subquery\n",
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" ) main left join \n",
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" (\n",
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" select subquery.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n",
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" subquery.SEGMENT,\n",
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" subquery.RISK,\n",
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" case\n",
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" when subquery.SAR_FLAG is NULL then 'N'\n",
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" else subquery.SAR_FLAG\n",
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" end as SAR_FLAG \n",
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" from\n",
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" (\n",
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" (\n",
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" select customer_number as CUSTOMER_NUMBER_3, \n",
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" business_segment as SEGMENT,\n",
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" case\n",
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" when RISK_CLASSIFICATION = 1 then 'Low Risk'\n",
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" when RISK_CLASSIFICATION = 2 then 'Medium Risk'\n",
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" when RISK_CLASSIFICATION = 3 then 'High Risk'\n",
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" else 'Unknown Risk'\n",
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" end AS RISK\n",
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" from {cust_data}\n",
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" ) cd left join\n",
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" (\n",
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" select customer_number as CUSTOMER_NUMBER_4, \n",
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" sar_flag as SAR_FLAG\n",
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" from {alert_data}\n",
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" ) ad on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4\n",
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" ) subquery\n",
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" ) cust_alert on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
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" ) final\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "d220561a-34c9-48d2-8e2f-5d174a87540b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from tms_data_interface import SQLQueryInterface\n",
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"\n",
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"class Scenario:\n",
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" seq = SQLQueryInterface(schema=\"transactionschema\")\n",
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"\n",
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" def logic(self, **kwargs):\n",
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" row_list = self.seq.execute_raw(query.format(trans_data=\"transaction10m\",\n",
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" cust_data=\"customer_data_v1\",\n",
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" acc_data=\"account_data_v1\",\n",
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" alert_data=\"alert_data_v1\")\n",
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" )\n",
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" cols = [\"Focal_id\", \"Credit_transaction_amount\",\n",
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" \"Total_no_of_credit_transactions\",\n",
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" \"Debit_transaction_amount\", \"Total_no_of_debit_transactions\",\n",
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" \"Wash_Ratio\", \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
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" df = pd.DataFrame(row_list, columns = cols)\n",
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" return df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "2e5a0ea9-64cd-4a8d-9a5d-e5e7b36a401a",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Focal_id</th>\n",
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" <th>Credit_transaction_amount</th>\n",
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" <th>Total_no_of_credit_transactions</th>\n",
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" <th>Debit_transaction_amount</th>\n",
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" <th>Total_no_of_debit_transactions</th>\n",
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" <th>Wash_Ratio</th>\n",
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" <th>Segment</th>\n",
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" <th>Risk</th>\n",
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" <th>SAR_FLAG</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>PN808624</td>\n",
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" <td>4.601504e+09</td>\n",
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" <td>3239</td>\n",
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" <td>4.461280e+09</td>\n",
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" <td>3129</td>\n",
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" <td>1.031431</td>\n",
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" <td>Corporate Banking</td>\n",
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" <td>Medium Risk</td>\n",
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" <td>N</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>PN663041</td>\n",
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" <td>2.106224e+09</td>\n",
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" <td>1573</td>\n",
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" <td>2.281829e+09</td>\n",
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" <td>1563</td>\n",
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" <td>0.923042</td>\n",
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" <td>Corporate Banking</td>\n",
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" <td>Low Risk</td>\n",
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" <td>N</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>PN525913</td>\n",
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" <td>1.057799e+09</td>\n",
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" <td>776</td>\n",
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" <td>1.223876e+09</td>\n",
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" <td>850</td>\n",
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" <td>0.864302</td>\n",
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" <td>Whole Sale Banking</td>\n",
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" <td>Low Risk</td>\n",
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" <td>N</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>PN440274</td>\n",
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" <td>4.806265e+09</td>\n",
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" <td>3506</td>\n",
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" <td>4.972813e+09</td>\n",
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" <td>3599</td>\n",
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" <td>0.966508</td>\n",
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" <td>Whole Sale Banking</td>\n",
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" <td>Medium Risk</td>\n",
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" <td>N</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>PN213026</td>\n",
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" <td>3.982349e+09</td>\n",
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" <td>2809</td>\n",
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" <td>4.122674e+09</td>\n",
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" <td>2783</td>\n",
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" <td>0.965963</td>\n",
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" <td>Whole Sale Banking</td>\n",
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" <td>Medium Risk</td>\n",
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" <td>N</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10009</th>\n",
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" <td>PN774741</td>\n",
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" <td>3.373466e+07</td>\n",
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" <td>250</td>\n",
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" <td>2.443148e+07</td>\n",
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" <td>381</td>\n",
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" <td>1.380787</td>\n",
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" <td>Priority Banking</td>\n",
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" <td>Medium Risk</td>\n",
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" <td>N</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10010</th>\n",
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" <td>PN868326</td>\n",
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" <td>3.785344e+07</td>\n",
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" <td>259</td>\n",
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" <td>2.408309e+07</td>\n",
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" <td>352</td>\n",
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" <td>1.571785</td>\n",
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" <td>Ultra High NetWorth</td>\n",
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" <td>Medium Risk</td>\n",
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" <td>Y</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10011</th>\n",
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" <td>PN667837</td>\n",
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" <td>3.330357e+07</td>\n",
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" <td>256</td>\n",
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" <td>2.676301e+07</td>\n",
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" <td>359</td>\n",
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" <td>1.244388</td>\n",
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" <td>Mass Market</td>\n",
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" <td>Medium Risk</td>\n",
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" <td>N</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10012</th>\n",
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" <td>PN809566</td>\n",
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" <td>3.890076e+07</td>\n",
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" <td>276</td>\n",
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" <td>2.554121e+07</td>\n",
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" <td>400</td>\n",
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" <td>1.523059</td>\n",
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" <td>Others</td>\n",
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" <td>Low Risk</td>\n",
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" <td>N</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10013</th>\n",
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" <td>PN739647</td>\n",
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" <td>3.505184e+07</td>\n",
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" <td>223</td>\n",
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" <td>2.232980e+07</td>\n",
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" <td>381</td>\n",
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" <td>1.569734</td>\n",
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" <td>Others</td>\n",
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" <td>Low Risk</td>\n",
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" <td>N</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>10014 rows × 9 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" Focal_id Credit_transaction_amount Total_no_of_credit_transactions \\\n",
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"0 PN808624 4.601504e+09 3239 \n",
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"1 PN663041 2.106224e+09 1573 \n",
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"2 PN525913 1.057799e+09 776 \n",
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"3 PN440274 4.806265e+09 3506 \n",
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"4 PN213026 3.982349e+09 2809 \n",
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"... ... ... ... \n",
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"10009 PN774741 3.373466e+07 250 \n",
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"10010 PN868326 3.785344e+07 259 \n",
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"10011 PN667837 3.330357e+07 256 \n",
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"10012 PN809566 3.890076e+07 276 \n",
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"10013 PN739647 3.505184e+07 223 \n",
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"\n",
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" Debit_transaction_amount Total_no_of_debit_transactions Wash_Ratio \\\n",
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"0 4.461280e+09 3129 1.031431 \n",
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"1 2.281829e+09 1563 0.923042 \n",
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"2 1.223876e+09 850 0.864302 \n",
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"3 4.972813e+09 3599 0.966508 \n",
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"4 4.122674e+09 2783 0.965963 \n",
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"... ... ... ... \n",
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"10009 2.443148e+07 381 1.380787 \n",
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"10010 2.408309e+07 352 1.571785 \n",
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"10011 2.676301e+07 359 1.244388 \n",
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"10012 2.554121e+07 400 1.523059 \n",
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"10013 2.232980e+07 381 1.569734 \n",
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"\n",
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" Segment Risk SAR_FLAG \n",
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"0 Corporate Banking Medium Risk N \n",
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"1 Corporate Banking Low Risk N \n",
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"2 Whole Sale Banking Low Risk N \n",
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"3 Whole Sale Banking Medium Risk N \n",
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"4 Whole Sale Banking Medium Risk N \n",
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"... ... ... ... \n",
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"10009 Priority Banking Medium Risk N \n",
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"10010 Ultra High NetWorth Medium Risk Y \n",
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"10011 Mass Market Medium Risk N \n",
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"10012 Others Low Risk N \n",
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"10013 Others Low Risk N \n",
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"\n",
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"[10014 rows x 9 columns]"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# sen = Scenario()\n",
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"# sen.logic()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "150bb5ce-6be1-44fc-a606-6d375354626d",
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"metadata": {},
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"outputs": [],
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"source": []
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||
}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
|