Update metrics/montoring_analytics.py

metrics/montoring_analytics.py

@@ -0,0 +1,86 @@
+# analytics/monitoring_analysis.py
+
+import pandas as pd
+
+from metrics.vintage_metrics import (
+    calculate_vintage_dpd,
+    calculate_vintage_ncl
+)
+
+
+def generate_monitoring_table(df):
+
+    dpd_30_mob3 = calculate_vintage_dpd(
+        df=df,
+        mob=3,
+        dpd_threshold=30,
+        metric_type="count"
+    )
+
+    dpd_30_mob6 = calculate_vintage_dpd(
+        df=df,
+        mob=6,
+        dpd_threshold=30,
+        metric_type="count"
+    )
+
+    dpd_60_mob6 = calculate_vintage_dpd(
+        df=df,
+        mob=6,
+        dpd_threshold=60,
+        metric_type="count"
+    )
+
+    ncl_y1 = calculate_vintage_ncl(
+        df=df,
+        mob=12
+    )
+
+    final_table = (
+        dpd_30_mob3[
+            ["reporting_month", "dpd_rate"]
+        ]
+        .rename(
+            columns={
+                "dpd_rate": "30+@3"
+            }
+        )
+    )
+
+    final_table = final_table.merge(
+        dpd_30_mob6[
+            ["reporting_month", "dpd_rate"]
+        ].rename(
+            columns={
+                "dpd_rate": "30+@6"
+            }
+        ),
+        on="reporting_month",
+        how="outer"
+    )
+
+    final_table = final_table.merge(
+        dpd_60_mob6[
+            ["reporting_month", "dpd_rate"]
+        ].rename(
+            columns={
+                "dpd_rate": "60+@6"
+            }
+        ),
+        on="reporting_month",
+        how="outer"
+    )
+
+    final_table = final_table.merge(
+        ncl_y1[
+            ["reporting_month", "ncl_rate"]
+        ].rename(
+            columns={
+                "ncl_rate": "Yr1_NCL"
+            }
+        ),
+        on="reporting_month",
+        how="outer"
+    )
+
+    return final_table