Code Sample, a copy-pastable example if possible

import numpy as np
import pandas as pd

df1 = pd.DataFrame(data={'col1': [1.1, 1.2]},
                   index=pd.MultiIndex.from_product([['A'], [1.0, 2.0]],
                                                    names=['id1', 'id2']))
df2 = pd.DataFrame(data={'col2': [2.1, 2.2]},
                   index=pd.MultiIndex.from_product([['A'], [np.NaN, 2.0]],
                                                    names=['id1', 'id2']))
print(df1.join(df2))

Problem description

The index containing the NaN value of df2 is falsely joined with the index of df1. Therefore, the result contains both values of df2 instead of only one value and a NaN value.

Expected Output

         col1  col2
id1 id2
A   1.0   1.1   NaN
    2.0   1.2   2.2

The expected result was returned by older pandas versions (successfully tested with 0.18.1 ; 0.20.3 ; 0.21.1 ; 0.22.0).

Actual Output

         col1  col2
id1 id2
A   1.0   1.1   2.1
    2.0   1.2   2.2

This result is returned by newer pandas versions, beginning with version 0.23.0 (tested with 0.23.0 ; 0.23.4 ; 0.24.2 ; 0.25.2).

I suppose this is a bug, or am I missing something?

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit : None python : 3.7.2.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 pandas : 0.25.2 numpy : 1.16.2 pytz : 2018.9 dateutil : 2.8.0 pip : 19.0.3 setuptools : 40.8.0 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 : None pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None lxml.etree : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None s3fs : None scipy : None sqlalchemy : None tables : None xarray : None xlrd : None xlwt : None xlsxwriter : None

Comment From: simonjayhawkins

master produces different output

>>> import numpy as np
>>> import pandas as pd
>>>
>>> pd.__version__
'0.26.0.dev0+684.g953757a3e'
>>>
>>> df1 = pd.DataFrame(
...     data={"col1": [1.1, 1.2]},
...     index=pd.MultiIndex.from_product([["A"], [1.0, 2.0]], names=["id1", "id2"]),
... )
>>> df2 = pd.DataFrame(
...     data={"col2": [2.1, 2.2]},
...     index=pd.MultiIndex.from_product([["A"], [np.NaN, 2.0]], names=["id1", "id2"]),
... )
>>> print(df1.join(df2))
         col1  col2
id1 id2
A   1.0   1.1   NaN
    2.0   1.2   NaN
>>>

Comment From: hanudev

In case someone else has the same problem, the following gives the expected output:

print(df1.join(df2, how='outer').loc[df1.index])

This is of course only a workaround, which is noticeably slower than a direct join.

Comment From: ezwelty

pandas 1.4.3

I just ran into this problem, and I would argue that it is more serious than first meets the eye. The behavior is inconsistent between pd.Index (correct) and pd.MultiIndex (wrong) and for pd.MultiIndex, between pd.DataFrame.join() (wrong) and pd.DataFrame.merge() (correct).

Say we want to join the following dataframes, and one has a missing value in the x column. (I use float64 here, but the behavior is the same with Float64, Int64, ... and pd.NA).

import pandas as pd

dfa = pd.DataFrame({
  'x': [1.0, float('nan')],
  'y': [1.0, 2.0]
})

dfb = pd.DataFrame({
  'x': [1.0, 2.0],
  'y': [1.0, 2.0],
  'z': [10.0, 20.0]
})

Joining on just x (with pd.Index) works as expected.

key = ['x']
ia = pd.Index(dfa['x'])
ib = pd.Index(dfb['x'])
ia.difference(ib).tolist()
# [nan]
ia.intersection(ib).tolist()
# [1.0]
dfa.set_index(key).join(dfb.set_index(key), how='inner', rsuffix='b')
#        y   yb     z
# x                  
# 1.0  1.0  1.0  10.0
dfa.merge(dfb, left_on=key, right_on=key, suffixes=('', 'b'))
#      x    y   yb     z
# 0  1.0  1.0  1.0  10.0

But joining on both x and y (with pd.MultiIndex) fails for pd.DataFrame.join somehow works correctly for pd.DataFrame.merge:

key = ['x', 'y']
ia = pd.MultiIndex.from_frame(dfa[key])
ib = pd.MultiIndex.from_frame(dfb[key])
ia.difference(ib).tolist()
# []
ia.intersection(ib)
# [(1.0, 1.0), (nan, 2.0)]
dfa.set_index(key).join(dfb.set_index(key), how='inner')
#             z
# x   y        
# 1.0 1.0  10.0
# NaN 2.0   NaN
dfa.merge(dfb, how='inner')
#      x    y     z
# 0  1.0  1.0  10.0