Index    Max_Mass (kg/m)    Max_Diameter (m)
1             10                   1
2             20                   2
3             30                   3

200           5                    4
201           60                   3
202           20                   2

300           90                   1
301           3                    1
302           10                   1

400           100                  1
401           10                   1
402           10                   1

groups = output_df.groupby(pd.cut(output_df.index, range(0,len(output_df), 100)))

groups.max()['Max Mass (kg/m)']

(groups.max()['Max Mass (kg/m)']).getidx()

Index    Max_Mass (kg/m)    Max_Diameter (m)
3             30                   3
201           60                   3
300           90                   1
400           100                  1

# Initialise the grouper.
grouper = df.Index // 100
# Get list of indices corresponding to the max using `apply`.
idx = df.groupby(grouper).apply(
          lambda x: x.set_index('Index')['Max_Mass (kg/m)'].idxmax())
# Compute the max and update the other columns based on `idx` computed previously.
v = df.groupby(grouper, as_index=False)['Max_Mass (kg/m)'].max()
v['Index'] = idx.values
v['Max_Diameter (m)'] = df.loc[df.Index.isin(v.Index), 'Max_Diameter (m)'].values

print(v)
   Max_Mass (kg/m)  Index  Max_Diameter (m)
0               30      3                 3
1               60    201                 3
2               90    300                 1
3              100    400                 1