您可以使用 boolean indexing 与

You can use boolean indexing with str.startswith:

cols = df.columns[df.columns.str.startswith('cd')]
print (cols)
Index(['cd_00', 'cd_01', 'cd_02', 'cd_90'], dtype='object')

示例:

print (df)
   col_00  col_01  col_02  col_51  bc_00  cd_00  cd_01  cd_02  cd_90
0       1       2       3       4      5      6      7      8      9

cols = df.columns[df.columns.str.startswith('cd')]
print (cols)
Index(['cd_00', 'cd_01', 'cd_02', 'cd_90'], dtype='object')

#if want apply some function for filtered columns only
def f(x):
    return x + 1

df[cols] = df[cols].apply(f)    
print (df)
   col_00  col_01  col_02  col_51  bc_00  cd_00  cd_01  cd_02  cd_90
0       1       2       3       4      5      7      8      9     10

使用list comprehension的另一种解决方案:

Another solution with list comprehension:

cols = [col for col in df.columns if col.startswith("cd")]
print (cols)
['cd_00', 'cd_01', 'cd_02', 'cd_90']