From an outsider's perspective, it would not be obvious that the rows from the output in step 8 represented 10-year intervals. One way to improve the index labels would be to show the beginning and end of each time interval. We can achieve this by concatenating the current index year with 9 added to itself:

>>> years = sal_final.index.year
>>> years_right = years + 9
>>> sal_final.index = years.astype(str) + '-' + years_right.astype(str)
>>> sal_final

There is actually a completely different way to do this recipe. We can use the cut function to create equal-width intervals based on the year that each employee was hired and form groups from it:

>>> cuts = pd.cut(employee.index.year, bins=5, precision=0)
>>> cuts.categories.values
array([Interval(1958.0, 1970.0, closed='right'),
       Interval(1970.0, 1981.0, closed='right'),
       Interval(1981.0, 1993.0, closed='right'),
       Interval(1993.0, 2004.0, closed='right'),
       Interval(2004.0, 2016.0, closed='right')], dtype=object)

>>> employee.groupby([cuts, 'GENDER'])['BASE_SALARY'] 
            .mean().unstack('GENDER').round(-2)