generated from dhairya/scenario_template
System save at 09/10/2024 09:40 by user_client2024
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user_client2024
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307
.ipynb_checkpoints/main-checkpoint.ipynb
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307
.ipynb_checkpoints/main-checkpoint.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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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\n",
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"\n",
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"query = \"\"\"\n",
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" select final.CUSTOMER_NUMBER_main as Focal_id,\n",
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" CAST(final.Cash_deposit_total AS DECIMAL(18, 2)) AS Cash_deposit_total,\n",
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" final.Cash_deposit_count,\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.Cash_deposit_total,\n",
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" subquery.Cash_deposit_count\n",
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" from \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 Cash_deposit_total, \n",
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" count(*) as Cash_deposit_count\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} trans_table \n",
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" left join {acc_data} acc_table\n",
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" on trans_table.benef_account_number = acc_table.account_number\n",
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" ) trans\n",
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" where account_number not in ('None')\n",
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" and transaction_desc = 'CASH RELATED TRANSACTION'\n",
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" group by customer_number\n",
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" ) subquery\n",
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" ) main \n",
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" left join \n",
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" (\n",
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" select cd.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n",
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" cd.SEGMENT,\n",
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" cd.RISK,\n",
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" case\n",
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" when ad.SAR_FLAG is NULL then 'N'\n",
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" else ad.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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" 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 \n",
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" 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 \n",
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" on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4\n",
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" ) as cust_alert\n",
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" on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
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") as final\n",
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"\"\"\"\n",
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"\n",
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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\", \"Cash_deposit_total\", \"Cash_deposit_count\",\n",
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" \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
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" df = pd.DataFrame(row_list, columns = cols)\n",
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" df[\"Cash_deposit_total\"] = df[\"Cash_deposit_total\"].astype(float)\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": 3,
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"id": "1f20337b-8116-47e5-8743-1ba41e2df819",
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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>Cash_deposit_total</th>\n",
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" <th>Cash_deposit_count</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>PN469304</td>\n",
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" <td>8049952.0</td>\n",
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" <td>141</td>\n",
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" <td>Mass Market</td>\n",
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" <td>High 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>PN605743</td>\n",
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" <td>9766101.0</td>\n",
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" <td>175</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>2</th>\n",
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" <td>PN399215</td>\n",
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" <td>8469943.0</td>\n",
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" <td>164</td>\n",
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" <td>Mass Market</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>PN175020</td>\n",
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" <td>17724959.0</td>\n",
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" <td>313</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>4</th>\n",
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" <td>PN345085</td>\n",
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" <td>7518628.0</td>\n",
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" <td>124</td>\n",
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" <td>Others</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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" </tr>\n",
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" <tr>\n",
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" <th>9005</th>\n",
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" <td>PN660329</td>\n",
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" <td>8528178.0</td>\n",
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" <td>141</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>9006</th>\n",
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" <td>PN336833</td>\n",
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" <td>8475036.0</td>\n",
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" <td>150</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>9007</th>\n",
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" <td>PN428534</td>\n",
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" <td>7871170.0</td>\n",
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" <td>129</td>\n",
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" <td>Priority 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>9008</th>\n",
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" <td>PN101660</td>\n",
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" <td>8450309.0</td>\n",
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" <td>142</td>\n",
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" <td>Mass Market</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>9009</th>\n",
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" <td>PN198641</td>\n",
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" <td>8690226.0</td>\n",
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" <td>166</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>9010 rows × 6 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" Focal_id Cash_deposit_total Cash_deposit_count Segment \\\n",
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"0 PN469304 8049952.0 141 Mass Market \n",
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"1 PN605743 9766101.0 175 Others \n",
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"2 PN399215 8469943.0 164 Mass Market \n",
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"3 PN175020 17724959.0 313 Others \n",
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"4 PN345085 7518628.0 124 Others \n",
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"... ... ... ... ... \n",
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"9005 PN660329 8528178.0 141 Others \n",
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"9006 PN336833 8475036.0 150 Others \n",
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"9007 PN428534 7871170.0 129 Priority Banking \n",
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"9008 PN101660 8450309.0 142 Mass Market \n",
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"9009 PN198641 8690226.0 166 Others \n",
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"\n",
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" Risk SAR_FLAG \n",
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"0 High Risk N \n",
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"1 Low Risk N \n",
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"2 Low Risk N \n",
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"3 Low Risk N \n",
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"4 Medium Risk N \n",
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"... ... ... \n",
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"9005 Low Risk N \n",
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"9006 Low Risk N \n",
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"9007 Low Risk N \n",
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"9008 Low Risk N \n",
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"9009 Low Risk N \n",
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"\n",
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"[9010 rows x 6 columns]"
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]
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},
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"execution_count": 3,
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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": "6de62b37-00d1-4c88-b27b-9a70e05add91",
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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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}
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340
main.ipynb
340
main.ipynb
@ -1,33 +1,307 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
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"metadata": {},
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"outputs": [],
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"source": "from tms_data_interface import SQLQueryInterface\n\nclass Scenario:\n\tseq = SQLQueryInterface()\n\n\tdef logic(self, **kwargs):\n\t\t# Write your code here\n"
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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": "3.8.13"
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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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}
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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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\n",
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"\n",
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"query = \"\"\"\n",
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" select final.CUSTOMER_NUMBER_main as Focal_id,\n",
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" CAST(final.Cash_deposit_total AS DECIMAL(18, 2)) AS Cash_deposit_total,\n",
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" final.Cash_deposit_count,\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.Cash_deposit_total,\n",
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" subquery.Cash_deposit_count\n",
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" from \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 Cash_deposit_total, \n",
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" count(*) as Cash_deposit_count\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} trans_table \n",
|
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" left join {acc_data} acc_table\n",
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" on trans_table.benef_account_number = acc_table.account_number\n",
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" ) trans\n",
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" where account_number not in ('None')\n",
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" and transaction_desc = 'CASH RELATED TRANSACTION'\n",
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" group by customer_number\n",
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" ) subquery\n",
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" ) main \n",
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" left join \n",
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" (\n",
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" select cd.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n",
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" cd.SEGMENT,\n",
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" cd.RISK,\n",
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" case\n",
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" when ad.SAR_FLAG is NULL then 'N'\n",
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" else ad.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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" 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 \n",
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" 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 \n",
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" on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4\n",
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" ) as cust_alert\n",
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" on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
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") as final\n",
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"\"\"\"\n",
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"\n",
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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\", \"Cash_deposit_total\", \"Cash_deposit_count\",\n",
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" \"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": 3,
|
||||
"id": "1f20337b-8116-47e5-8743-1ba41e2df819",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Focal_id</th>\n",
|
||||
" <th>Cash_deposit_total</th>\n",
|
||||
" <th>Cash_deposit_count</th>\n",
|
||||
" <th>Segment</th>\n",
|
||||
" <th>Risk</th>\n",
|
||||
" <th>SAR_FLAG</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>PN469304</td>\n",
|
||||
" <td>8049952.0</td>\n",
|
||||
" <td>141</td>\n",
|
||||
" <td>Mass Market</td>\n",
|
||||
" <td>High Risk</td>\n",
|
||||
" <td>N</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>PN605743</td>\n",
|
||||
" <td>9766101.0</td>\n",
|
||||
" <td>175</td>\n",
|
||||
" <td>Others</td>\n",
|
||||
" <td>Low Risk</td>\n",
|
||||
" <td>N</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>PN399215</td>\n",
|
||||
" <td>8469943.0</td>\n",
|
||||
" <td>164</td>\n",
|
||||
" <td>Mass Market</td>\n",
|
||||
" <td>Low Risk</td>\n",
|
||||
" <td>N</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>PN175020</td>\n",
|
||||
" <td>17724959.0</td>\n",
|
||||
" <td>313</td>\n",
|
||||
" <td>Others</td>\n",
|
||||
" <td>Low Risk</td>\n",
|
||||
" <td>N</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>PN345085</td>\n",
|
||||
" <td>7518628.0</td>\n",
|
||||
" <td>124</td>\n",
|
||||
" <td>Others</td>\n",
|
||||
" <td>Medium Risk</td>\n",
|
||||
" <td>N</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9005</th>\n",
|
||||
" <td>PN660329</td>\n",
|
||||
" <td>8528178.0</td>\n",
|
||||
" <td>141</td>\n",
|
||||
" <td>Others</td>\n",
|
||||
" <td>Low Risk</td>\n",
|
||||
" <td>N</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9006</th>\n",
|
||||
" <td>PN336833</td>\n",
|
||||
" <td>8475036.0</td>\n",
|
||||
" <td>150</td>\n",
|
||||
" <td>Others</td>\n",
|
||||
" <td>Low Risk</td>\n",
|
||||
" <td>N</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9007</th>\n",
|
||||
" <td>PN428534</td>\n",
|
||||
" <td>7871170.0</td>\n",
|
||||
" <td>129</td>\n",
|
||||
" <td>Priority Banking</td>\n",
|
||||
" <td>Low Risk</td>\n",
|
||||
" <td>N</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9008</th>\n",
|
||||
" <td>PN101660</td>\n",
|
||||
" <td>8450309.0</td>\n",
|
||||
" <td>142</td>\n",
|
||||
" <td>Mass Market</td>\n",
|
||||
" <td>Low Risk</td>\n",
|
||||
" <td>N</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9009</th>\n",
|
||||
" <td>PN198641</td>\n",
|
||||
" <td>8690226.0</td>\n",
|
||||
" <td>166</td>\n",
|
||||
" <td>Others</td>\n",
|
||||
" <td>Low Risk</td>\n",
|
||||
" <td>N</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>9010 rows × 6 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Focal_id Cash_deposit_total Cash_deposit_count Segment \\\n",
|
||||
"0 PN469304 8049952.0 141 Mass Market \n",
|
||||
"1 PN605743 9766101.0 175 Others \n",
|
||||
"2 PN399215 8469943.0 164 Mass Market \n",
|
||||
"3 PN175020 17724959.0 313 Others \n",
|
||||
"4 PN345085 7518628.0 124 Others \n",
|
||||
"... ... ... ... ... \n",
|
||||
"9005 PN660329 8528178.0 141 Others \n",
|
||||
"9006 PN336833 8475036.0 150 Others \n",
|
||||
"9007 PN428534 7871170.0 129 Priority Banking \n",
|
||||
"9008 PN101660 8450309.0 142 Mass Market \n",
|
||||
"9009 PN198641 8690226.0 166 Others \n",
|
||||
"\n",
|
||||
" Risk SAR_FLAG \n",
|
||||
"0 High Risk N \n",
|
||||
"1 Low Risk N \n",
|
||||
"2 Low Risk N \n",
|
||||
"3 Low Risk N \n",
|
||||
"4 Medium Risk N \n",
|
||||
"... ... ... \n",
|
||||
"9005 Low Risk N \n",
|
||||
"9006 Low Risk N \n",
|
||||
"9007 Low Risk N \n",
|
||||
"9008 Low Risk N \n",
|
||||
"9009 Low Risk N \n",
|
||||
"\n",
|
||||
"[9010 rows x 6 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
|
||||
101
main.py
Normal file
101
main.py
Normal file
@ -0,0 +1,101 @@
|
||||
#!/usr/bin/env python
|
||||
# coding: utf-8
|
||||
|
||||
# In[2]:
|
||||
|
||||
|
||||
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[3]:
|
||||
|
||||
|
||||
# sen = Scenario()
|
||||
# sen.logic()
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user