fix: Cleaning data

app.py

@@ -80,13 +80,9 @@ def clean_data(df):
         # Rename column
         df.rename(columns={"Hourly_Labor_Hours_Total": "value"}, inplace=True)
 
-    elif "Date_CY" in df.columns and "Hour" in df.columns and "Net_Sales_CY" in df.columns:
-        # Convert "Date" and "Hour" columns into datetime format
-        df["timestamp"] = pd.to_datetime(df["Date_CY"]) + pd.to_timedelta(df["Hour"].astype(int), unit='h')
-
-        # Handle the case where hour is 24
-        df.loc[df["timestamp"].dt.hour == 24, "timestamp"] = df["timestamp"] + pd.DateOffset(days=1)
-        df["timestamp"] = df["timestamp"].dt.floor('h')
+    elif "Date_CY" in df.columns and "Store" in df.columns and "Net_Sales_CY" in df.columns:
+        # Convert "Date_CY" and "Store" columns into datetime format
+        df["timestamp"] = pd.to_datetime(df["Date_CY"] + " " + df["Store"].astype(str) + ":00:00")
 
         # Keep only necessary columns
         df = df[["timestamp", "Net_Sales_CY"]]