unexpected behavior with NaN in multiIndex

[micheledemeo]

df = pd.DataFrame({'col':[1, 2, 3, 4, 5], 'ind1':['a','b','c','d',np.nan], 'ind2':[1,2,3,4,5] })
df.set_index(['ind1','ind2'],inplace=True)

df.loc['a']
#     col
# ind2
# 1     1

df.loc[['a']]
#            col
# ind1 ind2
# a    1       1
#      5       5

df.loc[['a']].reset_index()
#   ind1  ind2  col
# 0    a     1    1
# 1    a     5    5

Problem description

Is it normal replacing np.nan with 'a' ?

INSTALLED VERSIONS ------------------ commit: None python: 2.7.5.final.0 python-bits: 64 OS: Linux OS-release: 3.10.0-327.28.3.el7.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8

pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 32.0.0
Cython: None
numpy: 1.11.3
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None

[TomAugspurger]

Thanks for catching that. Nothing special about 'a' by the way. Any label on the outer level will show the same result.

[jreback]

hmm, indexing with nans if fraught with complication. the indexer must be off here.

pull requests to fix are welcome.

[theodoreschen]

I'm checking out pandas through the pycon dev sprint, and I'm taking a stab at the issue.

I'm able to replicate the bug, but instead of getting 'a' to replace the np.nan, I see np.nan (on a Windows box running Python3.6).

(I think) I've traced the issue down to the Index._join_level() function in pandas/core/indexes/base.py module. Will report back when I've nailed down the offending line.

[theodoreschen]

I think I found the issue...

In pandas/core/indexes/base.py, Index._join_level() is the following line:
new_lev_labels = algos.take_nd(rev_indexer, left.labels[level], allow_fill=False)

Running PDB these are the values going into algos.take_nd:

(Pdb) level
0
(Pdb) rev_indexer
array([ 0, -1, -1, -1], dtype=int64)
(Pdb) left
MultiIndex(levels=[['a', 'b', 'c', 'd'], [1, 2, 3, 4, 5]], labels=[[0, 1, 2, 3, -1], [0, 1, 2, 3, 4]], names=['ind1', 'ind2'])

algos.take_nd() pads 0's when allow_fill is False, pads with nan when allow_fill is True. The subsequent code takes any index of new_lev_label with 0's and returns the corresponding rows in the original DataFrame object (which is why the row labeled with np.NaN returned when it shouldn't).

I think the options are either prevent np.NaN values in indices or in base.py set allow_fill=True and replace nan's with -1.