Store  Item  WeekID  WeeklySales
1      1567   34      100.00
2      2765   34      86.00
3      1163   34      200.00
1      1567   35      160.00
.      .
.      .
.      .

weekly_sales_dict = {}
for i in df['WeekID'].unique():
    store_items_dict = {}
    subset = df[df['WeekID'] == i]
    subset = subset.groupby(['Store', 'Item']).agg({'WeeklySales':'sum'}).reset_index()
    for j in subset['Store'].unique():
        storeset = subset[subset['Store'] == j]
        store_items_dict.update({str(j): storeset})
    weekly_sales_dict.update({ str(i) : store_items_dict})

count = 0
key_list = list(df['WeekID'].unique())
lag, lag_list = {}, []
for k,v in weekly_sales_dict.items():
    if count != 0 and count != len(df['WeekID'].unique())-1:
        prev_wk = weekly_sales_dict[str(key_list[(count - 1)])]
        current_wk = weekly_sales_dict[str(key_list[count])
        for i in df['Store'].unique():
            prev_df = prev_wk[str(i)]
            current_df = current_wk[str(i)]
            for j in df['Item'].unique():
                print('in j')
                if j in list(current_df['Item'].unique()) and j in list(prev_df['Item'].unique()):
                    item_lag = current_df[current_df['Item'] == int(j)]['WeeklySales'].values - prev_df[prev_df['Item'] == int(j)]['WeeklySales'].values
                    df[df['Item'] == j][df['Store'] == i ][df['WeekID'] == key_list[count]]['lag'] = item_lag[0]
                    lag_list.append((str(i),str(j),item_lag[0]))
                elif j in list(current_df['Item'].unique()):
                    item_lag = current_df[current_df['Item'] == int(j)]['WeeklySales'].values
                    lag_list.append((str(i),str(j),item_lag[0]))
                else:
                    pass
        count += 1
    else:
        count += 1