adding pre and post average analysis

apps/kpi_analysis/trafic_analysis.py

@@ -145,30 +145,69 @@ def merge_and_compare(df_2g, df_3g, df_lte, pre_range, post_range, last_period_r
 
     comparison = df[df["period"].isin(["pre", "post"])]
 
-    pivot = (
+    sum_pivot = (
         comparison.groupby(["code", "period"])[
             ["total_voice_trafic", "total_data_trafic"]
         ]
         .sum()
         .unstack()
     )
-    pivot.columns = [f"{metric}_{period}" for metric, period in pivot.columns]
-    pivot = pivot.reset_index()
+    sum_pivot.columns = [f"{metric}_{period}" for metric, period in sum_pivot.columns]
+    sum_pivot = sum_pivot.reset_index()
 
     # Differences
-    pivot["total_voice_trafic_diff"] = (
-        pivot["total_voice_trafic_post"] - pivot["total_voice_trafic_pre"]
+    sum_pivot["total_voice_trafic_diff"] = (
+        sum_pivot["total_voice_trafic_post"] - sum_pivot["total_voice_trafic_pre"]
     )
-    pivot["total_data_trafic_diff"] = (
-        pivot["total_data_trafic_post"] - pivot["total_data_trafic_pre"]
+    sum_pivot["total_data_trafic_diff"] = (
+        sum_pivot["total_data_trafic_post"] - sum_pivot["total_data_trafic_pre"]
     )
 
     for metric in ["total_voice_trafic", "total_data_trafic"]:
-        pivot[f"{metric}_diff_pct"] = (
-            (pivot.get(f"{metric}_post", 0) - pivot.get(f"{metric}_pre", 0))
-            / pivot.get(f"{metric}_pre", 1)
+        sum_pivot[f"{metric}_diff_pct"] = (
+            (sum_pivot.get(f"{metric}_post", 0) - sum_pivot.get(f"{metric}_pre", 0))
+            / sum_pivot.get(f"{metric}_pre", 1)
         ) * 100
-    return df, last_period, pivot.round(2)
+
+    avg_pivot = (
+        comparison.groupby(["code", "period"])[
+            ["total_voice_trafic", "total_data_trafic"]
+        ]
+        .mean()
+        .unstack()
+    )
+    avg_pivot.columns = [f"{metric}_{period}" for metric, period in avg_pivot.columns]
+    avg_pivot = avg_pivot.reset_index()
+
+    # Differences
+    avg_pivot["total_voice_trafic_diff"] = (
+        avg_pivot["total_voice_trafic_post"] - avg_pivot["total_voice_trafic_pre"]
+    )
+    avg_pivot["total_data_trafic_diff"] = (
+        avg_pivot["total_data_trafic_post"] - avg_pivot["total_data_trafic_pre"]
+    )
+
+    for metric in ["total_voice_trafic", "total_data_trafic"]:
+        avg_pivot[f"{metric}_diff_pct"] = (
+            (avg_pivot.get(f"{metric}_post", 0) - avg_pivot.get(f"{metric}_pre", 0))
+            / avg_pivot.get(f"{metric}_pre", 1)
+        ) * 100
+
+    # rename avg_pivot columns
+    avg_pivot = avg_pivot.rename(
+        columns={
+            "total_voice_trafic_pre": "avg_voice_trafic_pre",
+            "total_voice_trafic_post": "avg_voice_trafic_post",
+            "total_voice_trafic_diff": "avg_voice_trafic_diff",
+            "total_voice_trafic_diff_pct": "avg_voice_trafic_diff_pct",
+            "total_data_trafic_pre": "avg_data_trafic_pre",
+            "total_data_trafic_post": "avg_data_trafic_post",
+            "total_data_trafic_diff": "avg_data_trafic_diff",
+            "total_data_trafic_diff_pct": "avg_data_trafic_diff_pct",
+        }
+    )
+
+    return df, last_period, sum_pivot.round(2), avg_pivot.round(2)
 
 
 def monthly_data_analysis(df: pd.DataFrame) -> pd.DataFrame:
@@ -261,8 +300,15 @@ if st.button("🔍 Run Analysis"):
     df_3g_clean = preprocess_3g(df_3g)
     df_lte_clean = preprocess_lte(df_lte)
 
-    full_df, last_period, summary_df = merge_and_compare(
-        df_2g_clean, df_3g_clean, df_lte_clean, pre_range, post_range, last_period_range
+    full_df, last_period, sum_pre_post_analysis, avg_pre_post_analysis = (
+        merge_and_compare(
+            df_2g_clean,
+            df_3g_clean,
+            df_lte_clean,
+            pre_range,
+            post_range,
+            last_period_range,
+        )
     )
 
     monthly_voice_df, monthly_data_df = monthly_data_analysis(full_df)
@@ -273,7 +319,7 @@ if st.button("🔍 Run Analysis"):
     # 🔍 Display Summary
     st.success("✅ Analysis completed")
     st.subheader("📈 Summary Analysis Pre / Post")
-    st.dataframe(summary_df)
+    st.dataframe(sum_pre_post_analysis)
     TraficAnalysis.last_period_df = last_period
 
 #######################################################################################################""
@@ -444,10 +490,17 @@ if TraficAnalysis.last_period_df is not None:
     st.plotly_chart(fig)
 
     final_dfs = convert_dfs(
-        [full_df, summary_df, monthly_voice_df, monthly_data_df],
+        [
+            full_df,
+            sum_pre_post_analysis,
+            avg_pre_post_analysis,
+            monthly_voice_df,
+            monthly_data_df,
+        ],
         [
             "Global_Trafic_Analysis",
-            "Pre_Post_analysis",
+            "Sum_pre_post_analysis",
+            "Avg_pre_post_analysis",
             "Monthly_voice_analysis",
             "Monthly_data_analysis",
         ],