PERF: Performance Discrepancy in MultiIndex Creation with and without Timezones

[HarryCollins2]

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Reproducible Example

Description:

Hey there,

I stumbled upon a bit of a head-scratcher while creating MultiIndexes. There's quite a performance difference when we create MultiIndexes with timezones compared to without.

# T
%time multi_idx_no_tz = pd.MultiIndex.from_arrays([np.repeat(pd.Timestamp('2019-01-01 00:00:00', tz=None), n), range(n)])
%time multi_idx_with_tz = pd.MultiIndex.from_arrays([np.repeat(pd.Timestamp('2019-01-01 00:00:00', tz='UTC'), n), range(n)])

CPU times: user 1.1 s, sys: 48.6 ms, total: 1.15 s
Wall time: 1.16 s
CPU times: user 349 ms, sys: 27 ms, total: 376 ms
Wall time: 379 ms

I am curious about the performance difference here and would appreciate your insights.

Thank you!

Installed Versions

INSTALLED VERSIONS

commit : a671b5a
python : 3.10.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.936

pandas : 2.1.4
numpy : 1.26.3
pytz : 2023.4
dateutil : 2.8.2
setuptools : 65.4.1
pip : 22.2.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : 8.20.0
pandas_datareader : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat: None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.6.0
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None

Prior Performance

No response

[jrmylow]

Hi, suspect it's the same root cause as #56860

My write up of this issue is here: #56860 (comment), unfortunately haven't gotten around to a fix yet

[mroeschke]

Thanks for the report. Closing as a duplicate of #56860