BUG or DOC: NaNs generated when enlarge DataFrame with loc[:, cols] with a DataFrame RHS

[sappersapper]

Pandas version checks

• I have checked that this issue has not already been reported.

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

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

Reproducible Example

import pandas as pd
import numpy as np

df = pd.DataFrame(np.ones([3, 3]), columns=list('abc'))
df.loc[:, ['d', 'e']] = df[['a', 'b']]
df

	a 	b 	c 	d 	e
0 	1.0 	1.0 	1.0 	NaN 	NaN
1 	1.0 	1.0 	1.0 	NaN 	NaN
2 	1.0 	1.0 	1.0 	NaN 	NaN

Issue Description

To enlarge a DataFrame, these three ways can get right result:

df[['d', 'e']] = df[['a', 'b']]

df.loc[:, ['d', 'e']] = df[['a', 'b']].to_numpy()

df.loc[:, 'd'] = df['a']
df.loc[:, 'e'] = df['b']

But after df.loc[:, ['d', 'e']] = df[['a', 'b']], new columns only contain NaNs, it seems inconsistent.

I find Warning on assignment with .loc[:, cols], but the warning is about assigning existing columns, and there is no description in the documentation for Setting with enlargement.

Is it appropriate to extend the behavior of .loc[:, newcols]? or enhance the doc? or raise warning?

Expected Behavior

	a	b	c	d	e
0	1.0	1.0	1.0	1.0	1.0
1	1.0	1.0	1.0	1.0	1.0
2	1.0	1.0	1.0	1.0	1.0

Installed Versions

INSTALLED VERSIONS

commit : 4bfe3d0
python : 3.9.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19043
machine : AMD64
processor : Intel64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Chinese (Simplified)_China.936

pandas : 1.4.2
numpy : 1.22.4
pytz : 2022.1
dateutil : 2.8.2
pip : 22.1.1
setuptools : 62.3.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.3.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli :
fastparquet : None
fsspec : None
gcsfs : None
markupsafe : 2.1.1
matplotlib : 3.5.1
numba : 0.54.0rc1+0.g9bed2ebb2.dirty
numexpr : None
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 3.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.8.1
snappy : None
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : 2022.3.0
xlrd : 2.0.1
xlwt : None
zstandard : None

[simonjayhawkins]

Thanks @sappersapper for the report.

The issue here is touched upon in the discussion and the third code sample in #46974

cc @jorisvandenbossche

[phofl]

Yep, this is the same behavior as in the other issue. loc aligns your columns while setitem does not. I think we can close here, since this is not a bug right now