generated from user_client2024/78
System save at 23/05/2025 20:44 by user_client2024
This commit is contained in:
user_client2024
parent
cd8cc24134
commit
1fc6a20fff
@ -159,58 +159,58 @@
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},
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"outputs": [],
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"source": [
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"def trx_count_sum_groupwise(data_filt_partywise): \n",
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" data_filt_partywise = data_filt_partywise.sort_values(by='transaction_amount') \n",
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" groupeddata = pd.DataFrame(columns=['group_no', 'trxn_cnt', 'trxn_sum_amt', \n",
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" 'MIN_LIMIT', 'PCT_RANGE'])\n",
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"# def trx_count_sum_groupwise(data_filt_partywise): \n",
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"# data_filt_partywise = data_filt_partywise.sort_values(by='transaction_amount') \n",
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"# groupeddata = pd.DataFrame(columns=['group_no', 'trxn_cnt', 'trxn_sum_amt', \n",
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"# 'MIN_LIMIT', 'PCT_RANGE'])\n",
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" \n",
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" trxns = data_filt_partywise['transaction_amount'].values\n",
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" pct_range = data_filt_partywise['PCT_RANGE'].max()\n",
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" min_value = data_filt_partywise['MIN_LIMIT'].max()\n",
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"# trxns = data_filt_partywise['transaction_amount'].values\n",
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"# pct_range = data_filt_partywise['PCT_RANGE'].max()\n",
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"# min_value = data_filt_partywise['MIN_LIMIT'].max()\n",
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"\n",
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" trxns = trxns[trxns >= min_value]\n",
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" if len(trxns) > 0:\n",
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" min_value = trxns[0]\n",
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"# trxns = trxns[trxns >= min_value]\n",
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"# if len(trxns) > 0:\n",
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"# min_value = trxns[0]\n",
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"\n",
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" group_count = 0\n",
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" while len(trxns) > 0:\n",
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" max_value = min_value + (pct_range * 0.01 * min_value)\n",
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" mask = np.logical_and(trxns >= min_value, trxns <= max_value)\n",
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" group_filter_trx = trxns[mask]\n",
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" trx_count = len(group_filter_trx)\n",
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" trx_sum = np.sum(group_filter_trx)\n",
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" group_count += 1\n",
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" groupeddata.loc[len(groupeddata)] = [group_count, trx_count, trx_sum, \n",
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" min_value, pct_range]\n",
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" trxns = trxns[trxns > max_value]\n",
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" if len(trxns) > 0:\n",
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" min_value = trxns[0]\n",
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"# group_count = 0\n",
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"# while len(trxns) > 0:\n",
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"# max_value = min_value + (pct_range * 0.01 * min_value)\n",
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"# mask = np.logical_and(trxns >= min_value, trxns <= max_value)\n",
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"# group_filter_trx = trxns[mask]\n",
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"# trx_count = len(group_filter_trx)\n",
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"# trx_sum = np.sum(group_filter_trx)\n",
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"# group_count += 1\n",
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"# groupeddata.loc[len(groupeddata)] = [group_count, trx_count, trx_sum, \n",
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"# min_value, pct_range]\n",
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"# trxns = trxns[trxns > max_value]\n",
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"# if len(trxns) > 0:\n",
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"# min_value = trxns[0]\n",
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"\n",
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" return groupeddata.to_dict('list')\n",
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"# return groupeddata.to_dict('list')\n",
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"\n",
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"# ---------------------------\n",
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"# Function 4: Run scenario 9\n",
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"# ---------------------------\n",
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"def scenario9_data(data1): \n",
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" grouped = data1.groupby('Focal_id')[['transaction_amount', 'MIN_LIMIT', 'PCT_RANGE']].apply(\n",
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" trx_count_sum_groupwise).reset_index()\n",
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"# # ---------------------------\n",
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"# # Function 4: Run scenario 9\n",
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"# # ---------------------------\n",
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"# def scenario9_data(data1): \n",
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"# grouped = data1.groupby('Focal_id')[['transaction_amount', 'MIN_LIMIT', 'PCT_RANGE']].apply(\n",
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"# trx_count_sum_groupwise).reset_index()\n",
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"\n",
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" df_list = []\n",
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" for i in grouped.index:\n",
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" df_party = pd.DataFrame(grouped.iloc[i, -1])\n",
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" df_party['Focal_id'] = grouped.loc[i, 'Focal_id']\n",
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" df_list.append(df_party)\n",
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"# df_list = []\n",
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"# for i in grouped.index:\n",
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"# df_party = pd.DataFrame(grouped.iloc[i, -1])\n",
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"# df_party['Focal_id'] = grouped.loc[i, 'Focal_id']\n",
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"# df_list.append(df_party)\n",
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"\n",
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" final_df = pd.concat(df_list, ignore_index=True) \n",
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" Segment = data1.groupby('Focal_id')['Segment'].agg('max').reset_index()\n",
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" Risk = data1.groupby('Focal_id')['Risk'].agg('max').reset_index()\n",
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" SAR_FLAG = data1.groupby('Focal_id')['SAR_FLAG'].agg('max').reset_index()\n",
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"# final_df = pd.concat(df_list, ignore_index=True) \n",
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"# Segment = data1.groupby('Focal_id')['Segment'].agg('max').reset_index()\n",
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"# Risk = data1.groupby('Focal_id')['Risk'].agg('max').reset_index()\n",
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"# SAR_FLAG = data1.groupby('Focal_id')['SAR_FLAG'].agg('max').reset_index()\n",
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" \n",
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" final_df = final_df.merge(Segment,on = 'Focal_id', how = 'left')\n",
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" final_df = final_df.merge(Risk,on = 'Focal_id', how = 'left')\n",
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" final_df = final_df.merge(SAR_FLAG,on = 'Focal_id', how = 'left')\n",
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"# final_df = final_df.merge(Segment,on = 'Focal_id', how = 'left')\n",
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"# final_df = final_df.merge(Risk,on = 'Focal_id', how = 'left')\n",
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"# final_df = final_df.merge(SAR_FLAG,on = 'Focal_id', how = 'left')\n",
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" \n",
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" return final_df\n",
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"# return final_df\n",
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" "
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]
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},
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@ -253,10 +253,10 @@
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" ]\n",
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" df = pd.DataFrame(row_list, columns = cols)\n",
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" df['Segment'] = 'SME'\n",
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" df['MIN_LIMIT'] = 50000\n",
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" df['PCT_RANGE'] = 20\n",
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"# df['MIN_LIMIT'] = 50000\n",
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"# df['PCT_RANGE'] = 20\n",
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" \n",
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" scenario_data = scenario9_data(df)\n",
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"# scenario_data = scenario9_data(df)\n",
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" \n",
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" return scenario_data"
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]
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92
main.ipynb
92
main.ipynb
@ -159,58 +159,58 @@
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},
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"outputs": [],
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"source": [
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"def trx_count_sum_groupwise(data_filt_partywise): \n",
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" data_filt_partywise = data_filt_partywise.sort_values(by='transaction_amount') \n",
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" groupeddata = pd.DataFrame(columns=['group_no', 'trxn_cnt', 'trxn_sum_amt', \n",
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" 'MIN_LIMIT', 'PCT_RANGE'])\n",
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"# def trx_count_sum_groupwise(data_filt_partywise): \n",
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"# data_filt_partywise = data_filt_partywise.sort_values(by='transaction_amount') \n",
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"# groupeddata = pd.DataFrame(columns=['group_no', 'trxn_cnt', 'trxn_sum_amt', \n",
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"# 'MIN_LIMIT', 'PCT_RANGE'])\n",
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" \n",
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" trxns = data_filt_partywise['transaction_amount'].values\n",
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" pct_range = data_filt_partywise['PCT_RANGE'].max()\n",
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" min_value = data_filt_partywise['MIN_LIMIT'].max()\n",
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"# trxns = data_filt_partywise['transaction_amount'].values\n",
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"# pct_range = data_filt_partywise['PCT_RANGE'].max()\n",
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"# min_value = data_filt_partywise['MIN_LIMIT'].max()\n",
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"\n",
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" trxns = trxns[trxns >= min_value]\n",
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" if len(trxns) > 0:\n",
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" min_value = trxns[0]\n",
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"# trxns = trxns[trxns >= min_value]\n",
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"# if len(trxns) > 0:\n",
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"# min_value = trxns[0]\n",
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"\n",
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" group_count = 0\n",
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" while len(trxns) > 0:\n",
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" max_value = min_value + (pct_range * 0.01 * min_value)\n",
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" mask = np.logical_and(trxns >= min_value, trxns <= max_value)\n",
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" group_filter_trx = trxns[mask]\n",
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" trx_count = len(group_filter_trx)\n",
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" trx_sum = np.sum(group_filter_trx)\n",
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" group_count += 1\n",
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" groupeddata.loc[len(groupeddata)] = [group_count, trx_count, trx_sum, \n",
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" min_value, pct_range]\n",
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" trxns = trxns[trxns > max_value]\n",
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" if len(trxns) > 0:\n",
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" min_value = trxns[0]\n",
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"# group_count = 0\n",
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"# while len(trxns) > 0:\n",
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"# max_value = min_value + (pct_range * 0.01 * min_value)\n",
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"# mask = np.logical_and(trxns >= min_value, trxns <= max_value)\n",
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"# group_filter_trx = trxns[mask]\n",
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"# trx_count = len(group_filter_trx)\n",
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"# trx_sum = np.sum(group_filter_trx)\n",
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"# group_count += 1\n",
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"# groupeddata.loc[len(groupeddata)] = [group_count, trx_count, trx_sum, \n",
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"# min_value, pct_range]\n",
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"# trxns = trxns[trxns > max_value]\n",
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"# if len(trxns) > 0:\n",
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"# min_value = trxns[0]\n",
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"\n",
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" return groupeddata.to_dict('list')\n",
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"# return groupeddata.to_dict('list')\n",
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"\n",
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"# ---------------------------\n",
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"# Function 4: Run scenario 9\n",
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"# ---------------------------\n",
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"def scenario9_data(data1): \n",
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" grouped = data1.groupby('Focal_id')[['transaction_amount', 'MIN_LIMIT', 'PCT_RANGE']].apply(\n",
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" trx_count_sum_groupwise).reset_index()\n",
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"# # ---------------------------\n",
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"# # Function 4: Run scenario 9\n",
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"# # ---------------------------\n",
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"# def scenario9_data(data1): \n",
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"# grouped = data1.groupby('Focal_id')[['transaction_amount', 'MIN_LIMIT', 'PCT_RANGE']].apply(\n",
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"# trx_count_sum_groupwise).reset_index()\n",
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"\n",
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" df_list = []\n",
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" for i in grouped.index:\n",
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" df_party = pd.DataFrame(grouped.iloc[i, -1])\n",
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" df_party['Focal_id'] = grouped.loc[i, 'Focal_id']\n",
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" df_list.append(df_party)\n",
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"# df_list = []\n",
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"# for i in grouped.index:\n",
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"# df_party = pd.DataFrame(grouped.iloc[i, -1])\n",
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"# df_party['Focal_id'] = grouped.loc[i, 'Focal_id']\n",
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"# df_list.append(df_party)\n",
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"\n",
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" final_df = pd.concat(df_list, ignore_index=True) \n",
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" Segment = data1.groupby('Focal_id')['Segment'].agg('max').reset_index()\n",
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" Risk = data1.groupby('Focal_id')['Risk'].agg('max').reset_index()\n",
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" SAR_FLAG = data1.groupby('Focal_id')['SAR_FLAG'].agg('max').reset_index()\n",
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"# final_df = pd.concat(df_list, ignore_index=True) \n",
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"# Segment = data1.groupby('Focal_id')['Segment'].agg('max').reset_index()\n",
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"# Risk = data1.groupby('Focal_id')['Risk'].agg('max').reset_index()\n",
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"# SAR_FLAG = data1.groupby('Focal_id')['SAR_FLAG'].agg('max').reset_index()\n",
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" \n",
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" final_df = final_df.merge(Segment,on = 'Focal_id', how = 'left')\n",
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" final_df = final_df.merge(Risk,on = 'Focal_id', how = 'left')\n",
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" final_df = final_df.merge(SAR_FLAG,on = 'Focal_id', how = 'left')\n",
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"# final_df = final_df.merge(Segment,on = 'Focal_id', how = 'left')\n",
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"# final_df = final_df.merge(Risk,on = 'Focal_id', how = 'left')\n",
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"# final_df = final_df.merge(SAR_FLAG,on = 'Focal_id', how = 'left')\n",
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" \n",
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" return final_df\n",
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"# return final_df\n",
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" "
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]
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},
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@ -253,10 +253,10 @@
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" ]\n",
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" df = pd.DataFrame(row_list, columns = cols)\n",
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" df['Segment'] = 'SME'\n",
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" df['MIN_LIMIT'] = 50000\n",
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" df['PCT_RANGE'] = 20\n",
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"# df['MIN_LIMIT'] = 50000\n",
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"# df['PCT_RANGE'] = 20\n",
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" \n",
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" scenario_data = scenario9_data(df)\n",
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"# scenario_data = scenario9_data(df)\n",
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" \n",
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" return scenario_data"
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]
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92
main.py
92
main.py
@ -142,58 +142,58 @@ query = """
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# In[20]:
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def trx_count_sum_groupwise(data_filt_partywise):
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data_filt_partywise = data_filt_partywise.sort_values(by='transaction_amount')
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groupeddata = pd.DataFrame(columns=['group_no', 'trxn_cnt', 'trxn_sum_amt',
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'MIN_LIMIT', 'PCT_RANGE'])
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# def trx_count_sum_groupwise(data_filt_partywise):
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# data_filt_partywise = data_filt_partywise.sort_values(by='transaction_amount')
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# groupeddata = pd.DataFrame(columns=['group_no', 'trxn_cnt', 'trxn_sum_amt',
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# 'MIN_LIMIT', 'PCT_RANGE'])
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trxns = data_filt_partywise['transaction_amount'].values
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pct_range = data_filt_partywise['PCT_RANGE'].max()
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min_value = data_filt_partywise['MIN_LIMIT'].max()
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# trxns = data_filt_partywise['transaction_amount'].values
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# pct_range = data_filt_partywise['PCT_RANGE'].max()
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# min_value = data_filt_partywise['MIN_LIMIT'].max()
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trxns = trxns[trxns >= min_value]
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if len(trxns) > 0:
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min_value = trxns[0]
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# trxns = trxns[trxns >= min_value]
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# if len(trxns) > 0:
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# min_value = trxns[0]
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group_count = 0
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while len(trxns) > 0:
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max_value = min_value + (pct_range * 0.01 * min_value)
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mask = np.logical_and(trxns >= min_value, trxns <= max_value)
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group_filter_trx = trxns[mask]
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trx_count = len(group_filter_trx)
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trx_sum = np.sum(group_filter_trx)
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group_count += 1
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groupeddata.loc[len(groupeddata)] = [group_count, trx_count, trx_sum,
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min_value, pct_range]
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trxns = trxns[trxns > max_value]
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if len(trxns) > 0:
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min_value = trxns[0]
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# group_count = 0
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# while len(trxns) > 0:
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# max_value = min_value + (pct_range * 0.01 * min_value)
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# mask = np.logical_and(trxns >= min_value, trxns <= max_value)
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# group_filter_trx = trxns[mask]
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# trx_count = len(group_filter_trx)
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# trx_sum = np.sum(group_filter_trx)
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# group_count += 1
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# groupeddata.loc[len(groupeddata)] = [group_count, trx_count, trx_sum,
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# min_value, pct_range]
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# trxns = trxns[trxns > max_value]
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# if len(trxns) > 0:
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# min_value = trxns[0]
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return groupeddata.to_dict('list')
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# return groupeddata.to_dict('list')
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# ---------------------------
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# Function 4: Run scenario 9
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# ---------------------------
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def scenario9_data(data1):
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grouped = data1.groupby('Focal_id')[['transaction_amount', 'MIN_LIMIT', 'PCT_RANGE']].apply(
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trx_count_sum_groupwise).reset_index()
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# # ---------------------------
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# # Function 4: Run scenario 9
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# # ---------------------------
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# def scenario9_data(data1):
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# grouped = data1.groupby('Focal_id')[['transaction_amount', 'MIN_LIMIT', 'PCT_RANGE']].apply(
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# trx_count_sum_groupwise).reset_index()
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df_list = []
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for i in grouped.index:
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df_party = pd.DataFrame(grouped.iloc[i, -1])
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df_party['Focal_id'] = grouped.loc[i, 'Focal_id']
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df_list.append(df_party)
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# df_list = []
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# for i in grouped.index:
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# df_party = pd.DataFrame(grouped.iloc[i, -1])
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# df_party['Focal_id'] = grouped.loc[i, 'Focal_id']
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# df_list.append(df_party)
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final_df = pd.concat(df_list, ignore_index=True)
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Segment = data1.groupby('Focal_id')['Segment'].agg('max').reset_index()
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Risk = data1.groupby('Focal_id')['Risk'].agg('max').reset_index()
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SAR_FLAG = data1.groupby('Focal_id')['SAR_FLAG'].agg('max').reset_index()
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# final_df = pd.concat(df_list, ignore_index=True)
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# Segment = data1.groupby('Focal_id')['Segment'].agg('max').reset_index()
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# Risk = data1.groupby('Focal_id')['Risk'].agg('max').reset_index()
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# SAR_FLAG = data1.groupby('Focal_id')['SAR_FLAG'].agg('max').reset_index()
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final_df = final_df.merge(Segment,on = 'Focal_id', how = 'left')
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final_df = final_df.merge(Risk,on = 'Focal_id', how = 'left')
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final_df = final_df.merge(SAR_FLAG,on = 'Focal_id', how = 'left')
|
||||
# final_df = final_df.merge(Segment,on = 'Focal_id', how = 'left')
|
||||
# final_df = final_df.merge(Risk,on = 'Focal_id', how = 'left')
|
||||
# final_df = final_df.merge(SAR_FLAG,on = 'Focal_id', how = 'left')
|
||||
|
||||
return final_df
|
||||
# return final_df
|
||||
|
||||
|
||||
|
||||
@ -229,10 +229,10 @@ class Scenario:
|
||||
]
|
||||
df = pd.DataFrame(row_list, columns = cols)
|
||||
df['Segment'] = 'SME'
|
||||
df['MIN_LIMIT'] = 50000
|
||||
df['PCT_RANGE'] = 20
|
||||
# df['MIN_LIMIT'] = 50000
|
||||
# df['PCT_RANGE'] = 20
|
||||
|
||||
scenario_data = scenario9_data(df)
|
||||
# scenario_data = scenario9_data(df)
|
||||
|
||||
return scenario_data
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user