System save at 23/05/2025 20:38 by user_client2024

.ipynb_checkpoints/main-checkpoint.ipynb

@@ -94,15 +94,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
-   "id": "b6c85de2-6a47-4109-8885-c138c289ec25",
+   "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": {

main.ipynb

@@ -94,15 +94,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
-   "id": "b6c85de2-6a47-4109-8885-c138c289ec25",
+   "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": {

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[ ]:
-
-
-
-