chore: drop missing values

app.py

@@ -87,6 +87,9 @@ def clean_data(df):
 
     elif "Date_CY" in df.columns and "Hour" in df.columns and "Net_Sales_CY" in df.columns:
         # Convert "Date_CY" and "Hour" columns into datetime format
+
+        df = df.dropna(subset=['Date_CY', 'Hour', 'Net_Sales_CY'])
+
         df["timestamp"] = pd.to_datetime(df["Date_CY"]) + pd.to_timedelta(df["Hour"].astype(int), unit='h')
 
         # Handle the case where hour is 24
@@ -101,7 +104,8 @@ def clean_data(df):
 
         # Drop rows where 'value' is NaN
         df = df.dropna(subset=['value'])
-        df = df.dropna(subset=['timestamp'])
+
+        # df = df.dropna(subset=['timestamp'])
 
         return df
 

sales_hourly_short.csv

@@ -1,33 +1,4 @@
 "Store","Date_CY","Date_PY","Hour","Destination","Gross_Sales_CY","Net_Sales_CY","Sales_Tax_CY","Discounts_CY","Voids_CY","Order_Ahead_Voids_CY","Tickets_CY","Tickets_NonZero_CY","Check_Calc_CY","Cash_Over_Short_CY","Outstanding_Checks_CY","Gross_Sales_PY","Net_Sales_PY","Sales_Tax_PY","Discounts_PY","Voids_PY","Order_Ahead_Voids_PY","Tickets_PY","Tickets_NonZero_PY","Check_Calc_PY","Cash_Over_Short_PY","Outstanding_Checks_PY"
-71a6d9a4,"12/30/2019","12/31/2018",6,"Stall",36.65,27.67,2.28,8.98,0.00,0.00,7,7,3.9529,"","",6.18,6.18,0.51,0.00,0.00,0.00,1,1,6.1800,"",""
-71a6d9a4,"12/30/2019","12/31/2018",7,"Stall",105.83,75.63,6.25,30.20,5.56,0.00,16,16,4.7269,"","",31.47,23.77,1.95,7.70,0.00,0.00,8,8,2.9713,"",""
-71a6d9a4,"12/30/2019","12/31/2018",8,"Stall",89.76,64.40,5.30,25.36,0.00,0.00,17,17,3.7882,"","",68.99,57.15,4.72,11.84,0.00,0.00,11,11,5.1955,"",""
-71a6d9a4,"12/30/2019","12/31/2018",9,"Stall",121.59,84.93,6.99,36.66,0.00,0.00,21,21,4.0443,"","",121.20,100.47,8.30,20.73,0.00,0.00,12,12,8.3725,"",""
-71a6d9a4,"12/30/2019","12/31/2018",10,"Stall",126.08,81.18,6.70,44.90,0.00,0.00,16,14,5.7986,"","",69.04,61.52,5.07,7.52,0.00,0.00,11,11,5.5927,"",""
-71a6d9a4,"12/30/2019","12/31/2018",11,"Stall",389.58,328.33,27.11,61.25,0.00,0.00,41,41,8.0080,"","",285.68,262.67,21.68,23.01,0.00,0.00,35,35,7.5049,"",""
-71a6d9a4,"12/30/2019","12/31/2018",12,"Stall",392.78,346.69,28.64,46.09,0.00,0.00,39,39,8.8895,"","",309.62,284.56,23.50,25.06,0.00,0.00,33,33,8.6230,"",""
-71a6d9a4,"12/30/2019","12/31/2018",13,"Stall",307.86,263.06,21.74,44.80,5.98,0.00,43,42,6.2633,"","",274.33,243.65,20.09,30.68,0.00,0.00,28,28,8.7018,"",""
-71a6d9a4,"12/30/2019","12/31/2018",14,"Stall",218.68,177.14,14.62,41.54,0.00,0.00,31,31,5.7142,"","",239.77,192.18,15.89,47.59,0.00,0.00,36,36,5.3383,"",""
-71a6d9a4,"12/30/2019","12/31/2018",15,"Stall",307.96,238.84,19.69,69.12,0.00,0.00,46,46,5.1922,"","",181.65,139.61,11.53,42.04,0.00,0.00,26,25,5.5844,"",""
-71a6d9a4,"12/30/2019","12/31/2018",16,"Stall",309.47,268.40,22.18,41.07,0.00,0.00,38,38,7.0632,"","",204.37,171.14,14.11,33.23,0.00,0.00,27,27,6.3385,"",""
-71a6d9a4,"12/30/2019","12/31/2018",17,"Stall",189.87,178.60,14.74,11.27,0.00,0.00,21,21,8.5048,"","",177.03,158.88,13.10,18.15,0.00,0.00,24,24,6.6200,"",""
-71a6d9a4,"12/30/2019","12/31/2018",18,"Stall",281.92,254.54,21.01,27.38,0.00,0.00,28,28,9.0907,"","",173.75,156.66,12.91,17.09,7.78,0.00,23,22,7.1209,"",""
-71a6d9a4,"12/30/2019","12/31/2018",19,"Stall",205.92,187.46,15.47,18.46,0.00,0.00,18,18,10.4144,"","",168.03,160.44,13.23,7.59,0.00,0.00,13,13,12.3415,"",""
-71a6d9a4,"12/30/2019","12/31/2018",20,"Stall",97.72,94.06,7.76,3.66,0.00,0.00,13,13,7.2354,"","",182.28,170.74,14.07,11.54,0.00,0.00,21,21,8.1305,"",""
-71a6d9a4,"12/30/2019","12/31/2018",21,"Stall",108.38,101.46,8.40,6.92,0.00,0.00,14,14,7.2471,"","",81.91,77.27,6.38,4.64,0.00,0.00,9,9,8.5856,"",""
-71a6d9a4,"12/30/2019","12/31/2018",22,"Stall",63.25,57.23,4.73,6.02,0.00,0.00,9,9,6.3589,"","","","","","","","","","","","",""
-71a6d9a4,"12/30/2019","12/31/2018",7,"Order Ahead",37.66,16.66,1.37,21.00,0.00,0.00,2,2,8.3300,"","","","","","","","","","","","",""
-71a6d9a4,"12/30/2019","12/31/2018",8,"Order Ahead",2.49,1.24,0.10,1.25,0.00,0.00,1,1,1.2400,"","","","","","","","","","","","",""
-71a6d9a4,"12/30/2019","12/31/2018",9,"Order Ahead",19.72,8.07,0.67,11.65,0.00,0.00,1,1,8.0700,"","","","","","","","","","","","",""
-71a6d9a4,"12/30/2019","12/31/2018",10,"Order Ahead",23.31,12.46,1.02,10.85,0.00,0.00,3,3,4.1533,"","","","","","","","","","","","",""
-71a6d9a4,"12/30/2019","12/31/2018",11,"Order Ahead",13.83,9.89,0.82,3.94,0.00,50.75,9,3,3.2967,"","","","","","","","","","","","",""
-71a6d9a4,"12/30/2019","12/31/2018",12,"Order Ahead",10.36,5.31,0.44,5.05,0.00,0.00,4,4,1.3275,"","",12.55,9.10,0.75,3.45,0.00,0.00,3,3,3.0333,"",""
-71a6d9a4,"12/30/2019","12/31/2018",13,"Order Ahead",21.33,17.30,1.43,4.03,0.00,0.00,3,3,5.7667,"","",7.56,3.57,0.29,3.99,0.00,0.00,1,1,3.5700,"",""
-71a6d9a4,"12/30/2019","12/31/2018",14,"Order Ahead",22.61,12.57,1.04,10.04,0.00,0.00,3,3,4.1900,"","","","","","","","","","","","",""
-71a6d9a4,"12/30/2019","12/31/2018",15,"Order Ahead",10.96,7.46,0.61,3.50,0.00,0.00,2,2,3.7300,"","","","","","","","","","","","",""
-71a6d9a4,"12/30/2019","12/31/2018",16,"Order Ahead",16.54,10.89,0.90,5.65,0.00,0.00,3,3,3.6300,"","",6.17,4.03,0.33,2.14,0.00,6.17,2,1,4.0300,"",""
-71a6d9a4,"12/30/2019","12/31/2018",17,"Order Ahead",59.91,54.06,4.46,5.85,0.00,0.00,5,5,10.8120,"","","","","","","","","","","","",""
-71a6d9a4,"12/30/2019","12/31/2018",18,"Order Ahead",10.05,6.70,0.55,3.35,0.00,0.00,2,2,3.3500,"","","","","","","","","","","","",""
 71a6d9a4,"12/30/2019","12/31/2018",19,"Order Ahead","","","","","","","","","","","",10.75,9.41,0.78,1.34,0.00,0.00,1,1,9.4100,"",""
 71a6d9a4,"12/30/2019","12/31/2018",20,"Order Ahead",9.57,7.89,0.65,1.68,0.00,0.00,1,1,7.8900,"","","","","","","","","","","","",""
 71a6d9a4,"12/30/2019","12/31/2018",9,"Delivery",25.11,0.70,0.06,24.41,0.00,0.00,3,1,0.7000,"","",6.57,0.00,0.00,6.57,0.00,0.00,1,0,"","",""