From ce047e1bb6cc62ce5d8d5711d8240b4fde1ee366 Mon Sep 17 00:00:00 2001 From: user_client2024 Date: Thu, 5 Dec 2024 06:39:43 +0000 Subject: [PATCH] System save at 05/12/2024 12:09 by user_client2024 --- .ipynb_checkpoints/main-checkpoint.ipynb | 4 ++-- main.ipynb | 4 ++-- main.py | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/.ipynb_checkpoints/main-checkpoint.ipynb b/.ipynb_checkpoints/main-checkpoint.ipynb index 8720304..7697184 100644 --- a/.ipynb_checkpoints/main-checkpoint.ipynb +++ b/.ipynb_checkpoints/main-checkpoint.ipynb @@ -105,10 +105,10 @@ " ]\n", " final_scenario_df = pd.DataFrame(row_list, columns = cols)\n", " final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME']/\\\n", - " final_scenario_df['TOTAL_VOLUME'] * 100\n", + " final_scenario_df['TOTAL_VOLUME'] * 100\n", " final_scenario_df['Segment'] = 'Default'\n", " final_scenario_df['SAR_FLAG'] = 'N'\n", - " final_scenario_df['Risk'] = 'Low Risk'\n", + " final_scenario_df['Risk'] = 'Medium Risk'\n", " final_scenario_df.dropna(inplace=True)\n", " # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\n", " return final_scenario_df\n" diff --git a/main.ipynb b/main.ipynb index 8720304..7697184 100644 --- a/main.ipynb +++ b/main.ipynb @@ -105,10 +105,10 @@ " ]\n", " final_scenario_df = pd.DataFrame(row_list, columns = cols)\n", " final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME']/\\\n", - " final_scenario_df['TOTAL_VOLUME'] * 100\n", + " final_scenario_df['TOTAL_VOLUME'] * 100\n", " final_scenario_df['Segment'] = 'Default'\n", " final_scenario_df['SAR_FLAG'] = 'N'\n", - " final_scenario_df['Risk'] = 'Low Risk'\n", + " final_scenario_df['Risk'] = 'Medium Risk'\n", " final_scenario_df.dropna(inplace=True)\n", " # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\n", " return final_scenario_df\n" diff --git a/main.py b/main.py index 6a08ef5..7bd671e 100644 --- a/main.py +++ b/main.py @@ -102,10 +102,10 @@ class Scenario: ] final_scenario_df = pd.DataFrame(row_list, columns = cols) final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME']/\ - final_scenario_df['TOTAL_VOLUME'] * 100 + final_scenario_df['TOTAL_VOLUME'] * 100 final_scenario_df['Segment'] = 'Default' final_scenario_df['SAR_FLAG'] = 'N' - final_scenario_df['Risk'] = 'Low Risk' + final_scenario_df['Risk'] = 'Medium Risk' final_scenario_df.dropna(inplace=True) # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE'] return final_scenario_df