DataFrame.nlargest result error

Code Sample, a copy-pastable example if possible

import pandas as pd
import numpy as np

df = pd.DataFrame({'a': [1, 10, 8, 11, 8],
    'b': list('abdce'),
    'c': [1.0, 2.0, np.nan, 3.0, 4.0]})
print('_________')
print(df.nlargest(10,['a','b']))

Problem description

DataFrame的nlargest在遇到rank相同的情况时，结果错误。如下，第二行和第四行反复出现了。

Expected Output

    a  b    c
3  11  c  3.0
1  10  b  2.0
2   8  d  NaN
4   8  e  4.0
2   8  d  NaN
4   8  e  4.0
0   1  a  1.0
[Finished in 0.6s]

Output of pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

[jreback]

@flystarhe your question is not clear

These might be what you want

In [5]: df.groupby(['a', 'b']).nth([0, 1, 2])
Out[5]: 
        c
a  b     
1  a  1.0
8  d  NaN
   e  4.0
10 b  2.0
11 c  3.0

In [6]: df.sort_values(['a', 'b']).groupby(['a', 'b']).head(10)
Out[6]: 
    a  b    c
0   1  a  1.0
2   8  d  NaN
4   8  e  4.0
1  10  b  2.0
3  11  c  3.0

[nasirudeenraheem]

better still since it does deals string format. so the df[‘’].value_counts().nlargest