BUG: Inconsistent assignment of NAT values to datetime columns in MultiIndex #43351
Description
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Code Sample, a copy-pastable example
>>> import pandas as pd >>> import numpy as np >>> midx = pd.MultiIndex( ... [ ... ["a", "b", "c"], ... ["house", "store", "forest"], ... ["clouds", "clear", "storm"], ... ["fire", "smoke", "clear"], ... [ ... np.datetime64("2001-01-01", "ns"), ... np.datetime64("2002-01-01", "ns"), ... np.datetime64("2003-01-01", "ns"), ... ], ... [1.0, np.nan, 2], ... ], ... [ ... [0, 0, 0, 0, 1, 1, 2], ... [1, 1, 1, 1, 0, 0, 2], ... [0, 0, 2, 2, 2, 0, 1], ... [0, 0, 0, 1, 2, 0, 1], ... [1, 0, 1, 2, 0, 0, 1], ... [1, 0, 1, 2, 0, 0, 1], ... ], ... ) >>> midx.names = ["alpha", "location", "weather", "sign", "timestamp", "float"] >>> >>> pdf = pd.DataFrame({'a':[1] , 'b':[2] , 'c':[3] , 'd':[2] , 'e':[0] , 'f':[2], 'g':[10]}) >>> pdf.columns = midx >>> pdf alpha a b c location store house forest weather clouds storm storm clouds clear sign fire fire smoke clear fire smoke timestamp 2002-01-01 2001-01-01 2002-01-01 2003-01-01 2001-01-01 2001-01-01 2002-01-01 float NaN 1.0 NaN 2.0 1.0 1.0 NaN 0 1 2 3 2 0 2 10 >>> pdf["new_col"] = [11] >>> pdf alpha a b c new_col location store house forest weather clouds storm storm clouds clear sign fire fire smoke clear fire smoke timestamp 2002-01-01 2001-01-01 2002-01-01 2003-01-01 2001-01-01 2001-01-01 2002-01-01 NaT float NaN 1.0 NaN 2.0 1.0 1.0 NaN 0 1 2 3 2 0 2 10 11 >>> pdf.columns MultiIndex([( 'a', 'store', 'clouds', 'fire', '2002-01-01', nan), ( 'a', 'store', 'clouds', 'fire', '2001-01-01', 1.0), ( 'a', 'store', 'storm', 'fire', '2002-01-01', nan), ( 'a', 'store', 'storm', 'smoke', '2003-01-01', 2.0), ( 'b', 'house', 'storm', 'clear', '2001-01-01', 1.0), ( 'b', 'house', 'clouds', 'fire', '2001-01-01', 1.0), ( 'c', 'forest', 'clear', 'smoke', '2002-01-01', nan), ('new_col', '', '', '', 'NaT', '')], names=['alpha', 'location', 'weather', 'sign', 'timestamp', 'float'])Problem description
It looks like just for datetime like types the NAT values are being inserted in multiIndex, and not for string or float types.
Previous behavior in 1.2.5 was consistent with all types, i.e., just insert empty string ''
[this should explain why the current behaviour is a problem and why the expected output is a better solution]
Expected Output
Consistency with all dtypes as in 1.2.5
Output of pd.show_versions()
INSTALLED VERSIONS
commit : 5f648bf
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 57.4.0
Cython : 0.29.24
pytest : 6.2.5
hypothesis : 6.17.4
sphinx : 4.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.27.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.08.1
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1