Description
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I have checked that this issue has not already been reported.
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I have confirmed this issue exists on the latest version of pandas.
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I have confirmed this issue exists on the master branch of pandas.
Reproducible Example
I'm a pytest dev that's been looking to speed up collection time, using pandas as a benchmark. I thought you'd be interested to know that there is a case which is slow (~1s) just because it does something heavy at the module scope:
pandas/pandas/tests/test_algos.py
Lines 1800 to 1805 in 6599834
These arrays are heavy to create, better to make it lazy by wrapping in a lambda or just split to two separate tests.
There's also something slow in the pandas/tests/reshape/merge/test_merge.py module but I'm not sure what.
Installed Versions
INSTALLED VERSIONS
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commit : 65998341034861527517756e6dd134dafea2b766
python : 3.9.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.14.6-arch1-1
Version : #1 SMP PREEMPT Sat, 18 Sep 2021 16:19:35 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.0.dev0+833.g6599834103
numpy : 1.21.2
pytz : 2021.3
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : 0.29.24
pytest : 6.3.0.dev745+g483f239d0
hypothesis : 6.23.1
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : None
pandas_datareader: None
bs4 : 4.10.0
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : None
tables : 3.6.1
tabulate : 0.8.9
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : None
Prior Performance
No response