generated from user_client2024/78
System save at 23/05/2025 20:38 by user_client2024
This commit is contained in:
user_client2024
parent
96eb5906ec
commit
383071036a
@ -94,15 +94,15 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "b6c85de2-6a47-4109-8885-c138c289ec25",
|
||||
"execution_count": 11,
|
||||
"id": "69d6771d-be1c-4ae1-802a-3ba7b2e8c5fb",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"import numpy as np\n",
|
||||
"query = \"\"\"\n",
|
||||
" SELECT \n",
|
||||
" t.transaction_key,\n",
|
||||
@ -146,7 +146,83 @@
|
||||
" ON p.customer_number = al.customer_number\n",
|
||||
"\n",
|
||||
" WHERE a.account_number IS NOT NULL\n",
|
||||
"\"\"\"\n",
|
||||
" limit 100\n",
|
||||
"\"\"\"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "82c2152f-513c-4fde-a4a9-6ee3a01ef897",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def trx_count_sum_groupwise(data_filt_partywise): \n",
|
||||
" data_filt_partywise = data_filt_partywise.sort_values(by='transaction_amount') \n",
|
||||
" groupeddata = pd.DataFrame(columns=['group_no', 'trxn_cnt', 'trxn_sum_amt', \n",
|
||||
" 'MIN_LIMIT', 'PCT_RANGE'])\n",
|
||||
" \n",
|
||||
" trxns = data_filt_partywise['transaction_amount'].values\n",
|
||||
" pct_range = data_filt_partywise['PCT_RANGE'].max()\n",
|
||||
" min_value = data_filt_partywise['MIN_LIMIT'].max()\n",
|
||||
"\n",
|
||||
" trxns = trxns[trxns >= min_value]\n",
|
||||
" if len(trxns) > 0:\n",
|
||||
" min_value = trxns[0]\n",
|
||||
"\n",
|
||||
" group_count = 0\n",
|
||||
" while len(trxns) > 0:\n",
|
||||
" max_value = min_value + (pct_range * 0.01 * min_value)\n",
|
||||
" mask = np.logical_and(trxns >= min_value, trxns <= max_value)\n",
|
||||
" group_filter_trx = trxns[mask]\n",
|
||||
" trx_count = len(group_filter_trx)\n",
|
||||
" trx_sum = np.sum(group_filter_trx)\n",
|
||||
" group_count += 1\n",
|
||||
" groupeddata.loc[len(groupeddata)] = [group_count, trx_count, trx_sum, \n",
|
||||
" min_value, pct_range]\n",
|
||||
" trxns = trxns[trxns > max_value]\n",
|
||||
" if len(trxns) > 0:\n",
|
||||
" min_value = trxns[0]\n",
|
||||
"\n",
|
||||
" return groupeddata.to_dict('list')\n",
|
||||
"\n",
|
||||
"# ---------------------------\n",
|
||||
"# Function 4: Run scenario 9\n",
|
||||
"# ---------------------------\n",
|
||||
"def scenario9_data(data1): \n",
|
||||
" grouped = data1.groupby('Focal_id')[['transaction_amount', 'MIN_LIMIT', 'PCT_RANGE']].apply(\n",
|
||||
" trx_count_sum_groupwise).reset_index()\n",
|
||||
"\n",
|
||||
" df_list = []\n",
|
||||
" for i in grouped.index:\n",
|
||||
" df_party = pd.DataFrame(grouped.iloc[i, -1])\n",
|
||||
" df_party['Focal_id'] = grouped.loc[i, 'Focal_id']\n",
|
||||
" df_list.append(df_party)\n",
|
||||
"\n",
|
||||
" final_df = pd.concat(df_list, ignore_index=True) \n",
|
||||
" Segment = final_df.groupby('Focal_id')['Segment'].agg('max').reset_index()\n",
|
||||
" Risk = final_df.groupby('Focal_id')['Risk'].agg('max').reset_index()\n",
|
||||
" SAR_FLAG = final_df.groupby('Focal_id')['SAR_FLAG'].agg('max').reset_index()\n",
|
||||
" \n",
|
||||
" final_df = final_df.merge(Segment,on = 'Focal_id', how = 'left')\n",
|
||||
" final_df = final_df.merge(Risk,on = 'Focal_id', how = 'left')\n",
|
||||
" final_df = final_df.merge(SAR_FLAG,on = 'Focal_id', how = 'left')\n",
|
||||
" \n",
|
||||
" return final_df\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "b6c85de2-6a47-4109-8885-c138c289ec25",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"from tms_data_interface import SQLQueryInterface\n",
|
||||
"\n",
|
||||
@ -177,7 +253,12 @@
|
||||
" ]\n",
|
||||
" df = pd.DataFrame(row_list, columns = cols)\n",
|
||||
" df['Segment'] = 'SME'\n",
|
||||
" return df"
|
||||
" df['MIN_LIMIT'] = 50000\n",
|
||||
" df['PCT_RANGE'] = 20\n",
|
||||
" \n",
|
||||
" scenario_data = scenario9_data(df)\n",
|
||||
" \n",
|
||||
" return scenario_data"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -192,14 +273,6 @@
|
||||
"# sen = Scenario()\n",
|
||||
"# sen.logic()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6de62b37-00d1-4c88-b27b-9a70e05add91",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
99
main.ipynb
99
main.ipynb
@ -94,15 +94,15 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "b6c85de2-6a47-4109-8885-c138c289ec25",
|
||||
"execution_count": 11,
|
||||
"id": "69d6771d-be1c-4ae1-802a-3ba7b2e8c5fb",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"import numpy as np\n",
|
||||
"query = \"\"\"\n",
|
||||
" SELECT \n",
|
||||
" t.transaction_key,\n",
|
||||
@ -146,7 +146,83 @@
|
||||
" ON p.customer_number = al.customer_number\n",
|
||||
"\n",
|
||||
" WHERE a.account_number IS NOT NULL\n",
|
||||
"\"\"\"\n",
|
||||
" limit 100\n",
|
||||
"\"\"\"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "82c2152f-513c-4fde-a4a9-6ee3a01ef897",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def trx_count_sum_groupwise(data_filt_partywise): \n",
|
||||
" data_filt_partywise = data_filt_partywise.sort_values(by='transaction_amount') \n",
|
||||
" groupeddata = pd.DataFrame(columns=['group_no', 'trxn_cnt', 'trxn_sum_amt', \n",
|
||||
" 'MIN_LIMIT', 'PCT_RANGE'])\n",
|
||||
" \n",
|
||||
" trxns = data_filt_partywise['transaction_amount'].values\n",
|
||||
" pct_range = data_filt_partywise['PCT_RANGE'].max()\n",
|
||||
" min_value = data_filt_partywise['MIN_LIMIT'].max()\n",
|
||||
"\n",
|
||||
" trxns = trxns[trxns >= min_value]\n",
|
||||
" if len(trxns) > 0:\n",
|
||||
" min_value = trxns[0]\n",
|
||||
"\n",
|
||||
" group_count = 0\n",
|
||||
" while len(trxns) > 0:\n",
|
||||
" max_value = min_value + (pct_range * 0.01 * min_value)\n",
|
||||
" mask = np.logical_and(trxns >= min_value, trxns <= max_value)\n",
|
||||
" group_filter_trx = trxns[mask]\n",
|
||||
" trx_count = len(group_filter_trx)\n",
|
||||
" trx_sum = np.sum(group_filter_trx)\n",
|
||||
" group_count += 1\n",
|
||||
" groupeddata.loc[len(groupeddata)] = [group_count, trx_count, trx_sum, \n",
|
||||
" min_value, pct_range]\n",
|
||||
" trxns = trxns[trxns > max_value]\n",
|
||||
" if len(trxns) > 0:\n",
|
||||
" min_value = trxns[0]\n",
|
||||
"\n",
|
||||
" return groupeddata.to_dict('list')\n",
|
||||
"\n",
|
||||
"# ---------------------------\n",
|
||||
"# Function 4: Run scenario 9\n",
|
||||
"# ---------------------------\n",
|
||||
"def scenario9_data(data1): \n",
|
||||
" grouped = data1.groupby('Focal_id')[['transaction_amount', 'MIN_LIMIT', 'PCT_RANGE']].apply(\n",
|
||||
" trx_count_sum_groupwise).reset_index()\n",
|
||||
"\n",
|
||||
" df_list = []\n",
|
||||
" for i in grouped.index:\n",
|
||||
" df_party = pd.DataFrame(grouped.iloc[i, -1])\n",
|
||||
" df_party['Focal_id'] = grouped.loc[i, 'Focal_id']\n",
|
||||
" df_list.append(df_party)\n",
|
||||
"\n",
|
||||
" final_df = pd.concat(df_list, ignore_index=True) \n",
|
||||
" Segment = final_df.groupby('Focal_id')['Segment'].agg('max').reset_index()\n",
|
||||
" Risk = final_df.groupby('Focal_id')['Risk'].agg('max').reset_index()\n",
|
||||
" SAR_FLAG = final_df.groupby('Focal_id')['SAR_FLAG'].agg('max').reset_index()\n",
|
||||
" \n",
|
||||
" final_df = final_df.merge(Segment,on = 'Focal_id', how = 'left')\n",
|
||||
" final_df = final_df.merge(Risk,on = 'Focal_id', how = 'left')\n",
|
||||
" final_df = final_df.merge(SAR_FLAG,on = 'Focal_id', how = 'left')\n",
|
||||
" \n",
|
||||
" return final_df\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "b6c85de2-6a47-4109-8885-c138c289ec25",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"from tms_data_interface import SQLQueryInterface\n",
|
||||
"\n",
|
||||
@ -177,7 +253,12 @@
|
||||
" ]\n",
|
||||
" df = pd.DataFrame(row_list, columns = cols)\n",
|
||||
" df['Segment'] = 'SME'\n",
|
||||
" return df"
|
||||
" df['MIN_LIMIT'] = 50000\n",
|
||||
" df['PCT_RANGE'] = 20\n",
|
||||
" \n",
|
||||
" scenario_data = scenario9_data(df)\n",
|
||||
" \n",
|
||||
" return scenario_data"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -192,14 +273,6 @@
|
||||
"# sen = Scenario()\n",
|
||||
"# sen.logic()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6de62b37-00d1-4c88-b27b-9a70e05add91",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
80
main.py
80
main.py
@ -87,11 +87,11 @@
|
||||
# return df
|
||||
|
||||
|
||||
# In[5]:
|
||||
# In[11]:
|
||||
|
||||
|
||||
import pandas as pd
|
||||
|
||||
import numpy as np
|
||||
query = """
|
||||
SELECT
|
||||
t.transaction_key,
|
||||
@ -135,8 +135,71 @@ query = """
|
||||
ON p.customer_number = al.customer_number
|
||||
|
||||
WHERE a.account_number IS NOT NULL
|
||||
limit 100
|
||||
"""
|
||||
|
||||
|
||||
# In[12]:
|
||||
|
||||
|
||||
def trx_count_sum_groupwise(data_filt_partywise):
|
||||
data_filt_partywise = data_filt_partywise.sort_values(by='transaction_amount')
|
||||
groupeddata = pd.DataFrame(columns=['group_no', 'trxn_cnt', 'trxn_sum_amt',
|
||||
'MIN_LIMIT', 'PCT_RANGE'])
|
||||
|
||||
trxns = data_filt_partywise['transaction_amount'].values
|
||||
pct_range = data_filt_partywise['PCT_RANGE'].max()
|
||||
min_value = data_filt_partywise['MIN_LIMIT'].max()
|
||||
|
||||
trxns = trxns[trxns >= min_value]
|
||||
if len(trxns) > 0:
|
||||
min_value = trxns[0]
|
||||
|
||||
group_count = 0
|
||||
while len(trxns) > 0:
|
||||
max_value = min_value + (pct_range * 0.01 * min_value)
|
||||
mask = np.logical_and(trxns >= min_value, trxns <= max_value)
|
||||
group_filter_trx = trxns[mask]
|
||||
trx_count = len(group_filter_trx)
|
||||
trx_sum = np.sum(group_filter_trx)
|
||||
group_count += 1
|
||||
groupeddata.loc[len(groupeddata)] = [group_count, trx_count, trx_sum,
|
||||
min_value, pct_range]
|
||||
trxns = trxns[trxns > max_value]
|
||||
if len(trxns) > 0:
|
||||
min_value = trxns[0]
|
||||
|
||||
return groupeddata.to_dict('list')
|
||||
|
||||
# ---------------------------
|
||||
# Function 4: Run scenario 9
|
||||
# ---------------------------
|
||||
def scenario9_data(data1):
|
||||
grouped = data1.groupby('Focal_id')[['transaction_amount', 'MIN_LIMIT', 'PCT_RANGE']].apply(
|
||||
trx_count_sum_groupwise).reset_index()
|
||||
|
||||
df_list = []
|
||||
for i in grouped.index:
|
||||
df_party = pd.DataFrame(grouped.iloc[i, -1])
|
||||
df_party['Focal_id'] = grouped.loc[i, 'Focal_id']
|
||||
df_list.append(df_party)
|
||||
|
||||
final_df = pd.concat(df_list, ignore_index=True)
|
||||
Segment = final_df.groupby('Focal_id')['Segment'].agg('max').reset_index()
|
||||
Risk = final_df.groupby('Focal_id')['Risk'].agg('max').reset_index()
|
||||
SAR_FLAG = final_df.groupby('Focal_id')['SAR_FLAG'].agg('max').reset_index()
|
||||
|
||||
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
|
||||
|
||||
|
||||
|
||||
# In[13]:
|
||||
|
||||
|
||||
from tms_data_interface import SQLQueryInterface
|
||||
|
||||
class Scenario:
|
||||
@ -166,7 +229,12 @@ class Scenario:
|
||||
]
|
||||
df = pd.DataFrame(row_list, columns = cols)
|
||||
df['Segment'] = 'SME'
|
||||
return df
|
||||
df['MIN_LIMIT'] = 50000
|
||||
df['PCT_RANGE'] = 20
|
||||
|
||||
scenario_data = scenario9_data(df)
|
||||
|
||||
return scenario_data
|
||||
|
||||
|
||||
# In[4]:
|
||||
@ -175,9 +243,3 @@ class Scenario:
|
||||
# sen = Scenario()
|
||||
# sen.logic()
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user