BUG: columns=[] creates other columns than columns=None on DataFrame construct

[JeremyVriens]

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
from pandas.testing import assert_frame_equal

df1 = pd.DataFrame()
df2 = pd.DataFrame(columns=[])

assert_frame_equal(df1, df2)  # AssertionError: DataFrame.columns are different

Issue Description

The columns are not set correctly when the columns argument is an empty array.

Expected Behavior

I'm expecting no AssertionError on the reproducable example, the same as with Pandas 1.5.3.

Installed Versions

INSTALLED VERSIONS

commit : 1a2e300
python : 3.9.13.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.22621
machine : AMD64
processor : AMD64 Family 25 Model 33 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Netherlands.1252
pandas : 2.0.0rc0
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
setuptools : 57.0.0
pip : 23.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : None
qtpy : None
pyqt5 : None

[JeremyVriens]

It has been introduced with this pull request: #49637

[mroeschke]

This is an intended change described in https://pandas.pydata.org/docs/dev/whatsnew/v2.0.0.html#empty-dataframes-series-will-now-default-to-have-a-rangeindex

[rhshadrach]

@JeremyVriens - for understanding behavior it may be helpful to consider the following four examples.

df1 = pd.DataFrame(columns=[1])
df2 = pd.DataFrame(columns=[])
print(df1.columns)
# Index([1], dtype='int64')
print(df2.columns)
# Index([], dtype='object')

df3 = pd.DataFrame([[1]])
df4 = pd.DataFrame()
print(df3.columns)
# RangeIndex(start=0, stop=1, step=1)
print(df4.columns)
# RangeIndex(start=0, stop=0, step=1)

[rhshadrach]

Closing - @JeremyVriens if you think something is being overlooked, please feel free to comment here and we can reopen.