How to Append results in loop to data frame by index

Question

I have data that consists of 10 items. Each item has 12 locations with sales for over a year. I want to calculate the moving averages for each item location week and append my results to the main table

Data Sample:

wkly_sales = 
ID      STORE  WEEK_NUM   TOTAL_NET_UNITS           
100001  1      1          4
100001  1      2          5
100001  1      3          6
100001  2      1          9.5
100001  2      2          6
100001  2      3          33
100002  1      1          1
100002  1      2          9
100002  1      3          9
100002  2      1          1
100002  2      2          1
100002  2      3          2

Here is my code attempt:

for item in wkly_sales['ID'].unique(): 
    items = wkly_sales[wkly_sales['ID']==item]
    for loc in items['STORE'].unique():
        locs = items[items['STORE']==loc]
        locs['QUANTITY_SMA1'] = locs.TOTAL_NET_UNITS.rolling(1,min_periods=1).mean().shift().fillna(0)
#        locs['QUANTITY_SMA5'] = locs.TOTAL_NET_UNITS.rolling(5, min_periods=5, center=False).mean().fillna(0)
#         items['QUANTITY_SMA1']  = locs['QUANTITY_SMA1'] 
        for index, i in locs.iterrows():
            wkly_sales.at[index, 'QUANTITY_SMA1'] = i['QUANTITY_SMA1']
#            wkly_sales.at[index, 'QUANTITY_SMA5'] = i['QUANTITY_SMA5']

The problem is I am not able to append the calculated moving averages to the main data frame by the indexes

Expected result:

wkly_sales = 
ID      STORE  WEEK_NUM   TOTAL_NET_UNITS   QUANTITY_SMA1           
100001  1      1          4.                     0
100001  1      2          5                      4 
100001  1      3          6                      5
100001  2      1          9.5                    6 
100001  2      2          6                     9.5 
100001  2      3          33                     6 
100002  1      1          1                      33
100002  1      2          9                      1 
100002  1      3          9                      9
100002  2      1          1                      9
100002  2      2          1                      1
100002  2      3          2                      1