BUG: DataFrame.to_records inconsistent data typing for Index vs MultiIndex

[lukemanley]

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

df = pd.DataFrame({
    1: list("AB"),
    2: list("CD"),
    3: list("EF"),
})

print(df.to_records(index=False).dtype)
print(df.set_index(1).to_records(index=True).dtype)
print(df.set_index([1, 2]).to_records(index=True).dtype)

Issue Description

Inconsistent numpy types for the three versions above. The MultiIndex version does not match the first two:

(numpy.record, [('1', 'O'), ('2', 'O'), ('3', 'O')])
(numpy.record, [('1', 'O'), ('2', 'O'), ('3', 'O')])
(numpy.record, [('1', '<U1'), ('2', '<U1'), ('3', 'O')])

Expected Behavior

I would expect all three cases to result in the same numpy dtypes.

Would changing the MultiIndex behavior to match the first two be acceptable? If so, I will submit a PR.

Installed Versions

INSTALLED VERSIONS

commit : df8acf4
python : 3.8.13.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.16.3-microsoft-standard-WSL2
Version : #1 SMP Fri Apr 2 22:23:49 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.5.0.dev0+867.gdf8acf4201.dirty
numpy : 1.22.4
pytz : 2022.1
dateutil : 2.8.2
setuptools : 62.3.2
pip : 22.1.2
Cython : 0.29.30
pytest : 7.1.2
hypothesis : 6.46.11
sphinx : 4.5.0
blosc : None
feather : None
xlsxwriter : 3.0.3
lxml.etree : 4.9.0
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 8.4.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : 1.3.4
brotli :
fastparquet : 0.8.1
fsspec : 2021.11.0
gcsfs : 2021.11.0
matplotlib : 3.5.2
numba : 0.53.1
numexpr : 2.8.0
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 8.0.0
pyreadstat : 1.1.7
pyxlsb : None
s3fs : 2021.11.0
scipy : 1.8.1
snappy :
sqlalchemy : 1.4.37
tables : 3.7.0
tabulate : 0.8.9
xarray : 2022.3.0
xlrd : 2.0.1
xlwt : 1.3.0
zstandard : None

[lukemanley]

Note that strings are not the only impacted dtype. Here is another example with dates:

import pandas as pd

df = pd.DataFrame({
    1: pd.date_range('2022-01-01', periods=2),
    2: pd.date_range('2022-01-01', periods=2),
    3: pd.date_range('2022-01-01', periods=2),
})

print(df.to_records(index=False).dtype)
print(df.set_index(1).to_records(index=True).dtype)
print(df.set_index([1, 2]).to_records(index=True).dtype)

(numpy.record, [('1', '<M8[ns]'), ('2', '<M8[ns]'), ('3', '<M8[ns]')])
(numpy.record, [('1', '<M8[ns]'), ('2', '<M8[ns]'), ('3', '<M8[ns]')])
(numpy.record, [('1', 'O'), ('2', 'O'), ('3', '<M8[ns]')])