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System save at 04/11/2024 16:00 by user_client2024

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user_client2024 2024-11-04 10:30:59 +00:00
parent 5ce8a42a66
commit e11ed053b3
3 changed files with 12 additions and 12 deletions
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8
.ipynb_checkpoints/main-checkpoint.ipynb
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@ -95,7 +95,7 @@
" cols = [\n", " cols = [\n",
" 'START_DATE_TIME',\n", " 'START_DATE_TIME',\n",
" 'END_DATE_TIME',\n", " 'END_DATE_TIME',\n",
" 'FOCAL_ID',\n", " 'Focal_id',\n",
" 'MIN_PRICE',\n", " 'MIN_PRICE',\n",
" 'MAX_PRICE',\n", " 'MAX_PRICE',\n",
" 'PRICE_CHANGE_PCT',\n", " 'PRICE_CHANGE_PCT',\n",
@ -106,9 +106,9 @@
" final_scenario_df = pd.DataFrame(row_list, columns = cols)\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['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['Segment'] = 1\n",
" final_scenario_df['SAR_FLAG'] = 'N'\n", " final_scenario_df['SAR_FLAG'] = 1\n",
" final_scenario_df['RISK'] = 'Medium Risk'\n", " final_scenario_df['Risk'] = 1\n",
" final_scenario_df.dropna(inplace=True)\n", " final_scenario_df.dropna(inplace=True)\n",
" # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\n", " # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\n",
" return final_scenario_df\n" " return final_scenario_df\n"

8
main.ipynb
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@ -95,7 +95,7 @@
" cols = [\n", " cols = [\n",
" 'START_DATE_TIME',\n", " 'START_DATE_TIME',\n",
" 'END_DATE_TIME',\n", " 'END_DATE_TIME',\n",
" 'FOCAL_ID',\n", " 'Focal_id',\n",
" 'MIN_PRICE',\n", " 'MIN_PRICE',\n",
" 'MAX_PRICE',\n", " 'MAX_PRICE',\n",
" 'PRICE_CHANGE_PCT',\n", " 'PRICE_CHANGE_PCT',\n",
@ -106,9 +106,9 @@
" final_scenario_df = pd.DataFrame(row_list, columns = cols)\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['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['Segment'] = 1\n",
" final_scenario_df['SAR_FLAG'] = 'N'\n", " final_scenario_df['SAR_FLAG'] = 1\n",
" final_scenario_df['RISK'] = 'Medium Risk'\n", " final_scenario_df['Risk'] = 1\n",
" final_scenario_df.dropna(inplace=True)\n", " final_scenario_df.dropna(inplace=True)\n",
" # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\n", " # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']\n",
" return final_scenario_df\n" " return final_scenario_df\n"

8
main.py
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@ -92,7 +92,7 @@ class Scenario:
cols = [ cols = [
'START_DATE_TIME', 'START_DATE_TIME',
'END_DATE_TIME', 'END_DATE_TIME',
'FOCAL_ID', 'Focal_id',
'MIN_PRICE', 'MIN_PRICE',
'MAX_PRICE', 'MAX_PRICE',
'PRICE_CHANGE_PCT', 'PRICE_CHANGE_PCT',
@ -103,9 +103,9 @@ class Scenario:
final_scenario_df = pd.DataFrame(row_list, columns = cols) final_scenario_df = pd.DataFrame(row_list, columns = cols)
final_scenario_df['PARTICIPANT_VOLUME_PCT'] = final_scenario_df['PARTICIPANT_VOLUME']/\ 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['Segment'] = 1
final_scenario_df['SAR_FLAG'] = 'N' final_scenario_df['SAR_FLAG'] = 1
final_scenario_df['RISK'] = 'Medium Risk' final_scenario_df['Risk'] = 1
final_scenario_df.dropna(inplace=True) final_scenario_df.dropna(inplace=True)
# final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE'] # final_scenario_df['RUN_DATE'] = final_scenario_df['END_DATE']
return final_scenario_df return final_scenario_df