Improve efficiency of groupby and apply custom function to improve performance

Question

I have sales data of the form:

ID,year,sales,bonus
1,2015,1000,500
1,2015,1400,590
1,2016,1100,200
1,2017,1200,800
1,2017,1700,300
1,2017,1900,510
1,2018,2000,560
1,2018,1700,600
2,2015,2000,400
2,2015,1450,580
2,2015,2100,300
2,2017,1400,770
2,2017,2700,330
2,2018,3900,610
2,2018,2000,530
2,2018,1700,700
3,2015,2900,406
3,2015,1450,580
3,2015,2100,300
3,2017,1450,777
3,2018,3100,330
3,2018,3900,610
3,2019,2000,530
3,2019,1900,730

I want to include two new columns to capture the 10th and 90th percentile for the sales and bonus column for each ID for each year (I appreciate this doesn't make much sense in this simplified dataset)

I have used the following approach:

def aggs(df, names):

    for i in range(0, len(names)):
        df[names[i] + '90'] = df[names[i]].quantile(.90)
        df[names[i] + '10'] = df[names[i]].quantile(.10)

    return df

sales = pd.read_csv("sales.csv")
names = ['sales', 'bonus']
sales2 = sales.groupby(['year', 'ID'], as_index=False)
sales2 = sales2.apply(aggs, names)

To produce

    ID  year    sales   bonus   sales90 sales10 bonus90 bonus10
0   1   2015    1000    500 1360.0  1040.0  581.0   509.0
1   1   2015    1400    590 1360.0  1040.0  581.0   509.0
2   1   2016    1100    200 1100.0  1100.0  200.0   200.0
3   1   2017    1200    800 1860.0  1300.0  742.0   342.0
4   1   2017    1700    300 1860.0  1300.0  742.0   342.0
5   1   2017    1900    510 1860.0  1300.0  742.0   342.0
6   1   2018    2000    560 1970.0  1730.0  596.0   564.0
7   1   2018    1700    600 1970.0  1730.0  596.0   564.0
8   2   2015    2000    400 2080.0  1560.0  544.0   320.0
9   2   2015    1450    580 2080.0  1560.0  544.0   320.0
10  2   2015    2100    300 2080.0  1560.0  544.0   320.0
11  2   2017    1400    770 2570.0  1530.0  726.0   374.0
12  2   2017    2700    330 2570.0  1530.0  726.0   374.0
13  2   2018    3900    610 3520.0  1760.0  682.0   546.0
14  2   2018    2000    530 3520.0  1760.0  682.0   546.0
15  2   2018    1700    700 3520.0  1760.0  682.0   546.0
16  3   2015    2900    406 2740.0  1580.0  545.2   321.2
17  3   2015    1450    580 2740.0  1580.0  545.2   321.2
18  3   2015    2100    300 2740.0  1580.0  545.2   321.2
19  3   2017    1450    777 1450.0  1450.0  777.0   777.0
20  3   2018    3100    330 3820.0  3180.0  582.0   358.0
21  3   2018    3900    610 3820.0  3180.0  582.0   358.0
22  3   2019    2000    530 1990.0  1910.0  710.0   550.0

Problem

The code works as desired but as the number of rows and columns increases, there are big performance issues. I also get the warning:

PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()

Can anyone advise how I could improve this approach? thanks!