Pandas: Is there a way to use something like 'droplevel' and in process, rename the other level using the dropped level labels as prefix/suffix?

Use list comprehension for set new column names:

df.columns = df.columns.map('_'.join)Or:df.columns = ['_'.join(col) for col in df.columns]

Sample:

df = pd.DataFrame({'A':[1,2,2,1],                   'B':[4,5,6,4],                   'C':[7,8,9,1],                   'D':[1,3,5,9]})print (df)   A  B  C  D0  1  4  7  11  2  5  8  32  2  6  9  53  1  4  1  9df = df.groupby('A').agg([max, min])df.columns = df.columns.map('_'.join)print (df)   B_max  B_min  C_max  C_min  D_max  D_minA                                          1      4      4      7      1      9      12      6      5      9      8      5      3

print (['_'.join(col) for col in df.columns])['B_max', 'B_min', 'C_max', 'C_min', 'D_max', 'D_min']df.columns = ['_'.join(col) for col in df.columns]print (df)   B_max  B_min  C_max  C_min  D_max  D_minA                                          1      4      4      7      1      9      12      6      5      9      8      5      3

If need prefix simple swap items of tuples:

df.columns = ['_'.join((col[1], col[0])) for col in df.columns]print (df)   max_B  min_B  max_C  min_C  max_D  min_DA                                          1      4      4      7      1      9      12      6      5      9      8      5      3

Another solution:

df.columns = ['{}_{}'.format(i[1], i[0]) for i in df.columns]print (df)   max_B  min_B  max_C  min_C  max_D  min_DA                                          1      4      4      7      1      9      12      6      5      9      8      5      3

If len of columns is big (10^6), then rather use to_series and str.join:

df.columns = df.columns.to_series().str.join('_')

Using @jezrael's setup

df = pd.DataFrame({'A':[1,2,2,1],                   'B':[4,5,6,4],                   'C':[7,8,9,1],                   'D':[1,3,5,9]})df = df.groupby('A').agg([max, min])

Assign new columns with

from itertools import starmapdef flat(midx, sep=''):    fstr = sep.join(['{}'] * midx.nlevels)    return pd.Index(starmap(fstr.format, midx))df.columns = flat(df.columns, '_')df