BUG: In Series all null-values are printed as NaN

[realead]

Pandas version checks

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• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the master branch of pandas.

Reproducible Example

import pandas as pd
import numpy as np
s=pd.Series([1, 2, 3, 4], [True, None, np.nan, pd.NaT])
print(s)

Issue Description

All null-keys are printed as NaN:

True    1
NaN     2
NaN     3
NaN     5

This is confusing

Expected Behavior

True    1
None    2
nan    3
NaT   5

would be clearer

Installed Versions

INSTALLED VERSIONS

commit : c950850
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-53-generic
Version : #74-Ubuntu SMP Fri Dec 2 15:59:10 UTC 2016
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.4.0.dev0+1594.gc950850
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 58.0.4
Cython : 0.29.24
pytest : 6.0.2
hypothesis : 5.28.0
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.6
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.8.2
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
zstandard : None

[realead]

This is the underlying issue in #45222 if we would like to fix it less superficial than in #45262.

[simonjayhawkins]

indeed. and just as indicted in #45222 (comment) the underlying index attribute is correct

s = pd.Series([1, 2, 3, 4], [True, None, np.nan, pd.NaT])
s.index

Index([True, None, nan, NaT], dtype='object')

and this appears to have been the case since at least 0.25.3

[realead]

We have a similar issue with DataFrames as well:

import pandas as pd
import numpy as np
s=pd.DataFrame([1, 2, 3, 4], [True, None, np.nan, pd.NaT])
print(s)

leads to

      0
True  1
NaN   2
NaN   3
NaN   4

[realead]

Another problematic example:

import pandas as pd
import numpy as np

d = {np.nan: [1, 2], None: [3, 4], pd.NaT: [6,7], True: [8,9]}
s=pd.DataFrame(data=d)

print(s.columns)
print(s)

leads to

Index([nan, None, NaT, True], dtype='object')
   NaN  NaN  NaN  True
0    1    3    6     8
1    2    4    7     9

here is the column index concerned

[jreback]

can u show the index itself here

[realead]

can u show the index itself here

it is Index([nan, None, NaT, True], dtype='object')

[jreback]

ok great so this is just a repr issue then