Display sparse multi-index of dataframe in Pandas

```python

In [1]: 
import pandas as pd
from io import StringIO 

data = """
code\tname\ttyp\tntf\n
A5411\tWD\tAF\t\n
A5411\tWD\tAF\t210194618\n
B5498\tSH\tNC\t\n
B5498\tSH\tNC\t210213014\n
"""
df = pd.read_table(StringIO(data))

In [2]: df.set_index(['name','code'])
Out[2]:
           typ          ntf
name code
WD   A5411  AF          NaN
     A5411  AF  210194618.0
SH   B5498  NC          NaN
     B5498  NC  210213014.0

```

#### Expected Output

I am expecting the output of In[2] should be something like Out[3]
```
In [3]: df.set_index(['name', 'code', 'typ'])
Out[3]:
                        ntf
name code  typ
WD   A5411 AF           NaN
           AF   210194618.0
SH   B5498 NC           NaN
           NC   210213014.0
```

#### Problem description
Not all the levels of groups are displayed in sparse style (only show one row for all same values). For out[2], the column `code` are also expected to be in that way

#### Output of ``pd.show_versions()``

<details>

[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.22.0
pytest: None
pip: 9.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.14.0
scipy: None
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: 2.5.0
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
