REGR: Series repr of object Index with bools and NaN is wrong

Code Sample, a copy-pastable example if possible

import pandas as pd
# a series with booleans and nan
pd.Series([False,True,True,pd.NA]).value_counts(dropna=False)
True     2
False    1
True     1
dtype: int64

# check the actual index of the value_counts result
pd.Series([False,True,True,pd.NA]).value_counts(dropna=False).index
Index([True, False, nan], dtype='object')

# a similar Serie, with ints instead of nans, seems to work ok
pd.Series([0,1,1,pd.NA]).value_counts(dropna=False)
1.0    2
0.0    1
NaN    1
dtype: int64

Problem description

As shown in the example, the repr of the value_counts result is apparently wrong when booleans are in the serie. It should report nan as a possible value, instead it maps it to True. Note that this is limited to series with boolean. A similar example with ints works ok.

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit : None python : 3.7.1.final.0 python-bits : 64 OS : Linux OS-release : 4.15.0-76-generic machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8

pandas : 1.0.1 numpy : 1.18.1 pytz : 2019.3 dateutil : 2.8.1 pip : 20.0.2 setuptools : 45.2.0 Cython : None pytest : 5.3.5 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : 2.8.4 (dt dec pq3 ext lo64) jinja2 : None IPython : 7.12.0 pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None lxml.etree : None matplotlib : 3.1.3 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pytest : 5.3.5 pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : 1.3.13 tables : None tabulate : None xarray : None xlrd : None xlwt : None xlsxwriter : None numba : None

[jorisvandenbossche]

And the bug should be somewhere in here:

In [1]: idx = pd.Index([True, False, np.nan], dtype=object) 

In [2]: idx.format() 
Out[2]: ['True ', 'False', 'False']

[ts2095]

Not sure if this is related but it seems similar:

numpy.nan shows as True in index.

>>> import pandas as pd
>>> import numpy as np
>>> s = pd.Series(range(3), index=[True, False, np.nan])
>>> s
True     0
False    1
True     2
dtype: int64

This True is different from the regular True in the value count:

>>> s.index.value_counts(dropna=False)
True     1
False    1
True     1
dtype: int64

But the correct value is still used “under the hood”:

>>> s.index.unique()
Index([True, False, nan], dtype='object')