From b341970f51e2ae69897d8bd6550e8bc9a5b0f643 Mon Sep 17 00:00:00 2001 From: user_client2024 Date: Thu, 27 Nov 2025 06:04:35 +0000 Subject: [PATCH] System save at 27/11/2025 11:34 by user_client2024 --- .ipynb_checkpoints/main-checkpoint.ipynb | 22 +++++++++++++--------- main.ipynb | 22 +++++++++++++--------- main.py | 20 ++++++++++++-------- 3 files changed, 38 insertions(+), 26 deletions(-) diff --git a/.ipynb_checkpoints/main-checkpoint.ipynb b/.ipynb_checkpoints/main-checkpoint.ipynb index d0ae983..10a09d0 100644 --- a/.ipynb_checkpoints/main-checkpoint.ipynb +++ b/.ipynb_checkpoints/main-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 42, "id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0", "metadata": { "tags": [] @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 43, "id": "2f9a4ca7-c066-4d93-9957-0d9145f9265d", "metadata": { "tags": [] @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 44, "id": "134d0b3d-5481-4975-af07-c80ab09d6dd2", "metadata": { "tags": [] @@ -157,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 45, "id": "d220561a-34c9-48d2-8e2f-5d174a87540b", "metadata": { "tags": [] @@ -180,7 +180,11 @@ " \"Debit_transaction_amount\", \"Total_no_of_debit_transactions\",\n", " \"Wash_Ratio\", \"Segment\", \"Risk\", \"SAR_FLAG\"]\n", " df = pd.DataFrame(row_list, columns = cols)\n", - " \n", + " df[[\"Credit_transaction_amount\",\n", + " \"Debit_transaction_amount\",\n", + " \"Wash_Ratio\"]] = df[[\"Credit_transaction_amount\",\n", + " \"Debit_transaction_amount\",\n", + " \"Wash_Ratio\"]].astype('int')\n", " # Step 1: Compute 90th percentiles per Segment for all 3 fields\n", " percentiles = (\n", " df.groupby(\"Segment\")[[\"Credit_transaction_amount\",\n", @@ -219,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 51, "id": "2e5a0ea9-64cd-4a8d-9a5d-e5e7b36a401a", "metadata": { "tags": [] @@ -232,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 50, "id": "830c7ec3-9707-46db-9b27-ac4f9d46a03a", "metadata": { "tags": [] @@ -244,14 +248,14 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 49, "id": "150bb5ce-6be1-44fc-a606-6d375354626d", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# a[a[\"SAR_FLAG\"] == \"Y\"]" + "# a[a[\"SAR_FLAG\"] == \"Y\"]\n" ] } ], diff --git a/main.ipynb b/main.ipynb index d0ae983..10a09d0 100644 --- a/main.ipynb +++ b/main.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 42, "id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0", "metadata": { "tags": [] @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 43, "id": "2f9a4ca7-c066-4d93-9957-0d9145f9265d", "metadata": { "tags": [] @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 44, "id": "134d0b3d-5481-4975-af07-c80ab09d6dd2", "metadata": { "tags": [] @@ -157,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 45, "id": "d220561a-34c9-48d2-8e2f-5d174a87540b", "metadata": { "tags": [] @@ -180,7 +180,11 @@ " \"Debit_transaction_amount\", \"Total_no_of_debit_transactions\",\n", " \"Wash_Ratio\", \"Segment\", \"Risk\", \"SAR_FLAG\"]\n", " df = pd.DataFrame(row_list, columns = cols)\n", - " \n", + " df[[\"Credit_transaction_amount\",\n", + " \"Debit_transaction_amount\",\n", + " \"Wash_Ratio\"]] = df[[\"Credit_transaction_amount\",\n", + " \"Debit_transaction_amount\",\n", + " \"Wash_Ratio\"]].astype('int')\n", " # Step 1: Compute 90th percentiles per Segment for all 3 fields\n", " percentiles = (\n", " df.groupby(\"Segment\")[[\"Credit_transaction_amount\",\n", @@ -219,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 51, "id": "2e5a0ea9-64cd-4a8d-9a5d-e5e7b36a401a", "metadata": { "tags": [] @@ -232,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 50, "id": "830c7ec3-9707-46db-9b27-ac4f9d46a03a", "metadata": { "tags": [] @@ -244,14 +248,14 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 49, "id": "150bb5ce-6be1-44fc-a606-6d375354626d", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# a[a[\"SAR_FLAG\"] == \"Y\"]" + "# a[a[\"SAR_FLAG\"] == \"Y\"]\n" ] } ], diff --git a/main.py b/main.py index 98e9fe8..3e7f4ba 100644 --- a/main.py +++ b/main.py @@ -1,13 +1,13 @@ #!/usr/bin/env python # coding: utf-8 -# In[4]: +# In[42]: import pandas as pd -# In[5]: +# In[43]: from tms_data_interface import SQLQueryInterface @@ -20,7 +20,7 @@ seq = SQLQueryInterface(schema="transactionschema") seq.execute_raw("show tables") -# In[6]: +# In[44]: query = """ @@ -114,7 +114,7 @@ query = """ """ -# In[34]: +# In[45]: from tms_data_interface import SQLQueryInterface @@ -133,7 +133,11 @@ class Scenario: "Debit_transaction_amount", "Total_no_of_debit_transactions", "Wash_Ratio", "Segment", "Risk", "SAR_FLAG"] df = pd.DataFrame(row_list, columns = cols) - + df[["Credit_transaction_amount", + "Debit_transaction_amount", + "Wash_Ratio"]] = df[["Credit_transaction_amount", + "Debit_transaction_amount", + "Wash_Ratio"]].astype('int') # Step 1: Compute 90th percentiles per Segment for all 3 fields percentiles = ( df.groupby("Segment")[["Credit_transaction_amount", @@ -170,20 +174,20 @@ class Scenario: return df -# In[40]: +# In[51]: # sen = Scenario() # a = sen.logic() -# In[39]: +# In[50]: # a -# In[38]: +# In[49]: # a[a["SAR_FLAG"] == "Y"]