Add City to DataBase

process_kpi/process_wcel_capacity.py

@@ -291,7 +291,7 @@ def wcel_kpi_analysis(
     physical_db = physical_db.drop_duplicates(subset="code")
 
     # keep only code and longitude and latitude
-    physical_db = physical_db[["code", "Longitude", "Latitude"]]
+    physical_db = physical_db[["code", "Longitude", "Latitude", "City"]]
 
     physical_db["code"] = (
         pd.to_numeric(physical_db["code"], errors="coerce").fillna(0).astype(int)

queries/process_site_db.py

@@ -11,6 +11,7 @@ GSM_COLUMNS = [
     "Longitude",
     "Latitude",
     "Hauteur",
+    "City",
 ]
 
 WCDMA_COLUMNS = [
@@ -21,6 +22,7 @@ WCDMA_COLUMNS = [
     "Longitude",
     "Latitude",
     "Hauteur",
+    "City",
 ]
 LTE_COLUMNS = [
     "code",
@@ -30,6 +32,7 @@ LTE_COLUMNS = [
     "Longitude",
     "Latitude",
     "Hauteur",
+    "City",
 ]
 
 CODE_COLUMNS = [
@@ -38,6 +41,7 @@ CODE_COLUMNS = [
     "Longitude",
     "Latitude",
     "Hauteur",
+    "City",
 ]
 
 
@@ -150,6 +154,7 @@ def site_db():
             "Longitude",
             "Latitude",
             "Hauteur",
+            "City",
         ]
     ]
 

utils/convert_to_excel.py

@@ -139,6 +139,7 @@ def get_format_map_by_format_type(formats: dict, format_type: str) -> dict:
             "Longitude": formats["green"],
             "Latitude": formats["green"],
             "Hauteur": formats["green"],
+            "City": formats["green"],
             "number_trx_per_cell": formats["blue_light"],
             "number_trx_per_bcf": formats["blue_light"],
             "number_trx_per_site": formats["blue_light"],

utils/utils_vars.py

@@ -15,7 +15,9 @@ def get_physical_db():
         pd.DataFrame: A DataFrame containing the filtered columns.
     """
     physical = pd.read_csv(url)
-    physical = physical[["Code_Sector", "Azimut", "Longitude", "Latitude", "Hauteur"]]
+    physical = physical[
+        ["Code_Sector", "Azimut", "Longitude", "Latitude", "Hauteur", "City"]
+    ]
     return physical