Description
-
I have checked that this issue has not already been reported.
-
I have confirmed this bug exists on the latest version of pandas.
-
(optional) I have confirmed this bug exists on the master branch of pandas.
Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.
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