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generated from dhairya/scenario_template
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System save at 04/11/2024 15:58 by user_client2024

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user_client2024 2024-11-04 10:28:57 +00:00
parent 960c553359
commit 5ce8a42a66
3 changed files with 9 additions and 9 deletions
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6
.ipynb_checkpoints/main-checkpoint.ipynb
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@ -95,7 +95,7 @@
" cols = [\n",
" 'START_DATE_TIME',\n",
" 'END_DATE_TIME',\n",
" 'Focal_id',\n",
" 'FOCAL_ID',\n",
" 'MIN_PRICE',\n",
" 'MAX_PRICE',\n",
" 'PRICE_CHANGE_PCT',\n",
@ -106,9 +106,9 @@
" 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['Segment'] = 'Default'\n",
" final_scenario_df['SEGMENT'] = 'Default'\n",
" final_scenario_df['SAR_FLAG'] = 'N'\n",
" # final_scenario_df['Risk'] = 'Medium 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"

6
main.ipynb
View File

@ -95,7 +95,7 @@
" cols = [\n",
" 'START_DATE_TIME',\n",
" 'END_DATE_TIME',\n",
" 'Focal_id',\n",
" 'FOCAL_ID',\n",
" 'MIN_PRICE',\n",
" 'MAX_PRICE',\n",
" 'PRICE_CHANGE_PCT',\n",
@ -106,9 +106,9 @@
" 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['Segment'] = 'Default'\n",
" final_scenario_df['SEGMENT'] = 'Default'\n",
" final_scenario_df['SAR_FLAG'] = 'N'\n",
" # final_scenario_df['Risk'] = 'Medium 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"

6
main.py
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@ -92,7 +92,7 @@ class Scenario:
cols = [
'START_DATE_TIME',
'END_DATE_TIME',
'Focal_id',
'FOCAL_ID',
'MIN_PRICE',
'MAX_PRICE',
'PRICE_CHANGE_PCT',
@ -103,9 +103,9 @@ 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['Segment'] = 'Default'
final_scenario_df['SEGMENT'] = 'Default'
final_scenario_df['SAR_FLAG'] = 'N'
# final_scenario_df['Risk'] = 'Medium 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