Index value changes unexpectedly
#### Code Sample, a copy-pastable example if possible
First we create a raw dataset with MultiIndex-

```
In [166]: import numpy as np; import pandas as pd 

In [167]: data_raw = pd.DataFrame([ 
     ...: {'frame': 1, 'face': np.NaN, 'lmark': np.NaN, 'x': np.NaN, 'y': np.NaN}, 
     ...: {'frame': 197, 'face': 0, 'lmark': 1, 'x': 969, 'y': 737}, 
     ...: {'frame': 197, 'face': 0, 'lmark': 2, 'x': 969, 'y': 740}, 
     ...: {'frame': 197, 'face': 0, 'lmark': 3, 'x': 970, 'y': 744}, 
     ...: {'frame': 197, 'face': 0, 'lmark': 4, 'x': 972, 'y': 748}, 
     ...: {'frame': 197, 'face': 0, 'lmark': 5, 'x': 973, 'y': 752}, 
     ...: {'frame': 300, 'face': 0, 'lmark': 1, 'x': 745, 'y': 367},  
     ...: {'frame': 300, 'face': 0, 'lmark': 2, 'x': 753, 'y': 411},  
     ...: {'frame': 300, 'face': 0, 'lmark': 3, 'x': 759, 'y': 455}, 
     ...: {'frame': 301, 'face': 0, 'lmark': 1, 'x': 741, 'y': 364},   
     ...: {'frame': 301, 'face': 0, 'lmark': 2, 'x': 746, 'y': 408},   
     ...: {'frame': 301, 'face': 0, 'lmark': 3, 'x': 750, 'y': 452}]).set_index(['frame', 'face', 'lmark'])
```
Next we calculate the z-scores for each lmark -

```
In [168]: ((data_raw - data_raw.mean(level='lmark')).abs()) / data_raw.std(level='lmark')            
Out[168]: 
                         x         y
frame face lmark                    
1     NaN  NaN         NaN       NaN
197   0.0  1.0    1.154565  1.154672
           2.0    1.154260  1.154665
           3.0    1.153946  1.154654
           4.0         NaN       NaN
           5.0         NaN       NaN
300   0.0  1.0    0.561956  0.570343
           2.0    0.549523  0.569472
           3.0    0.540829  0.568384
301   0.0  1.0    0.592609  0.584329
           2.0    0.604738  0.585193
           3.0    0.613117  0.586270
```
The index values don't change, as expected. Now we filter out records where lmark > 3 -

```
[170]: data_filtered = data_raw.loc[(slice(None), slice(None), [np.NaN, slice(3)]),:]

In [171]: data_filtered                                                                          
Out[171]: 
                      x      y
frame face lmark              
1     NaN  NaN      NaN    NaN
197   0.0  1.0    969.0  737.0
           2.0    969.0  740.0
           3.0    970.0  744.0
300   0.0  1.0    745.0  367.0
           2.0    753.0  411.0
           3.0    759.0  455.0
301   0.0  1.0    741.0  364.0
           2.0    746.0  408.0
           3.0    750.0  452.0
```
and recalculate the z-scores -

```
In [172]: ((data_filtered - data_filtered.mean(level='lmark')).abs()) / data_filtered.std(level='lmark')                                                                                       
Out[172]: 
                         x         y
frame face lmark                    
1     NaN  1.0         NaN       NaN
197   0.0  1.0    1.154565  1.154672
           2.0    1.154260  1.154665
           3.0    1.153946  1.154654
300   0.0  1.0    0.561956  0.570343
           2.0    0.549523  0.569472
           3.0    0.540829  0.568384
301   0.0  1.0    0.592609  0.584329
           2.0    0.604738  0.585193
           3.0    0.613117  0.586270
```
#### Problem description

The value of the first record's lmark index changed from NaN to 1.0

#### Expected Output

The index values should not change

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

<details>

```
In [9]: pd.show_versions()

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.8.2.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : English_Netherlands.1252

pandas           : 1.0.3
numpy            : 1.18.2
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 41.2.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : 7.13.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.2.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None
```

</details>
