BUG: `DataFrame.loc` is not consistent with `DataFrame.__setitem__` when used with 2D numpy array

[anmyachev]

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.zeros((256, 10)))
array_2d = np.zeros((256, 2))

df[0] = array_2d  # works
df["col33"] = array_2d  # raises exception, see #46545
df.loc[:,0] = array_2d  # *** ValueError: Must have equal len keys and value when setting with an ndarray
# Note: if you swap 2 lines from above, then the code will work!

Issue Description

Inconsistent behavior of functions that should behave the same.

Expected Behavior

Either an exception should be thrown for both cases, or it should not, but also in both cases.

Installed Versions

INSTALLED VERSIONS

commit : 06d2301
python : 3.8.12.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19044
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252

pandas : 1.4.1
numpy : 1.22.2
pytz : 2021.3
dateutil : 2.8.2
pip : 22.0.3
setuptools : 59.8.0
Cython : None
pytest : 7.0.0
hypothesis : None
sphinx : 4.4.0
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.7.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 8.0.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
fsspec : 2022.01.0
gcsfs : None
matplotlib : 3.2.2
numba : None
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : 0.17.0
pyarrow : 6.0.1
pyreadstat : None
pyxlsb : None
s3fs : 2022.01.0
scipy : 1.8.0
sqlalchemy : 1.4.31
tables : 3.7.0
tabulate : None
xarray : 0.21.1
xlrd : 2.0.1
xlwt : None
zstandard : None

[phofl]

I think the loc case is correct

[simonjayhawkins]

I think the loc case is correct

for the case in the OP (without the edit) or for the swapped case ("Note: if you swap 2 lines from above, then the code will work!") or both?

Note this is a change in behavior from 1.3.5 when it worked for both cases and ordering did not matter.

I'll label as a regression, for now pending further investigation.

[phofl]

All of them should raise

[simonjayhawkins]

to be clear, on main...

df = pd.DataFrame(np.zeros((256, 10)))
array_2d = np.zeros((256, 2))

df.loc[:, 0] = array_2d
df[0] = array_2d

works

df = pd.DataFrame(np.zeros((256, 10)))
array_2d = np.zeros((256, 2))

df[0] = array_2d
df.loc[:, 0] = array_2d

raises

All of them should raise

i'm ignoring the df["col33"] = array_2d case as that was added to the OP later.

so both the above code samples (in this comment) should raise and there is a bug on master?

Note this is a change in behavior from 1.3.5 when it worked for both cases and ordering did not matter.

i'll do a bisect shortly to get more insight.

[phofl]

Yes I think so, if you use a list as indexer, e.g. [0], they are already raising.

also if you initial dataframe has multiple dtypes and we are running through the split path, they are also raising.

you can test this through adding df[100] = „a“ before doing the 2d assignment

[simonjayhawkins]

first bad commit: [03dd698] BUG: DataFrame.__setitem__ sometimes operating inplace (#43406)

yet it is the loc case that has changed behavior, only after a __setitem__ operation.