BUG: Constructing a df using 2D numpy arrays raises misleading error

[loodvn]

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• 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.

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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
>>> a = [[1], [2]]
>>> pd.DataFrame({'a': np.array(a), 'b': np.array(a)}) 
ValueError: If using all scalar values, you must pass an index

Problem description

Hey all! Thanks for the library, I use pandas on a daily basis.

When attempting to create a DataFrame with a 2-dimensional numpy array as input, a misleading ValueError: If using all scalar values, you must pass an index is raised instead of raising an expected ValueError: Data must be 1-dimensional.

This behaviour changes slightly if there is more than one array:

>>> import pandas as pd
>>> import numpy as np
>>> a = [[1], [2]]
>>> pd.DataFrame({'a': a, 'b': a})   # No error, both nested lists pass validation and index is extracted
     a    b
0  [1]  [1]
1  [2]  [2]
>>> pd.DataFrame({'a': np.array(a), 'b': a})   # Expected behaviour, the nested list passes the check at pd.core.internals.construction._extract_index
ValueError: Data must be 1-dimensional
>>> pd.DataFrame({'a': np.array(a), 'b': np.array(a)}) 
ValueError: If using all scalar values, you must pass an index

I suspect this may have something to do with the check at

pandas/pandas/core/internals/construction.py

Line 620 in 386bad4

elif is_list_like(val) and getattr(val, "ndim", 1) == 1:

not being exhaustive and/or that the shape validation only happens after this index creation step.

Expected Output

>>> pd.DataFrame({'a': np.array(a), 'b': np.array(a)}) 
ValueError: Data must be 1-dimensional

Output of pd.show_versions()

INSTALLED VERSIONS

commit : f00ed8f
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.104+
Version : #1 SMP Sat Jun 5 09:50:34 PDT 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.3.0
numpy : 1.19.5
pytz : 2018.9
dateutil : 2.8.1
pip : 19.3.1
setuptools : 57.0.0
Cython : 0.29.23
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 5.5.0
pandas_datareader: 0.9.0
bs4 : 4.6.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.3
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.13.3
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.4.20
tables : 3.4.4
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 1.1.0
xlwt : 1.3.0
numba : 0.51.2

[jbrockmendel]

can you add an informative title to the issue

[debnathshoham]

take