Unspecific error message when setting singular index with np dtype

[JoElfner]

Code Sample, a copy-pastable example if possible

some_np_array = np.array([43, 56])

# unspecific error message 'TypeError: len() of unsized object':
pd.DataFrame([4324, 345], index=some_np_array[0])
pd.Series([4324, 345], index=some_np_array[0])

# specific error message 'TypeError: Index(...) must be called with a collection of some kind, 5 was passed':
pd.DataFrame([4324, 345], index=5)
pd.Series([4324, 345], index=5)

Problem description

When passing a non-collection type as an index, pandas typically raises a quite helpful error message of the following form:
TypeError: Index(...) must be called with a collection of some kind, 5 was passed

Whereas when passing a non-standard type non-collection item as an index, pandas seems to raise some unspecific and misleading error:
TypeError: len() of unsized object

Expected Output

Specific error message, even when using np.float64, np.int64 etc. as type of singular values.

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None

pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.1.post20200323
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.48.0

[dsaxton]

I suppose it could raise a better error message (which likely shouldn't change if the value happens to come from inside a numpy array). The behavior now is because even the singleton np.array([1, 2])[0] has an __array__ attribute so the scalar check gets skipped: https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L398