PERF: Surprisingly slow nlargest with duplicates in the index · Issue #55767 · pandas-dev/pandas (original) (raw)

Pandas version checks

• I have checked that this issue has not already been reported.
• I have confirmed this issue exists on the latest version of pandas.
• I have confirmed this issue exists on the main branch of pandas.

Reproducible Example

N = 1500
slow_df = pd.DataFrame({'a': np.arange(N)}, index=[pd.NA] * N)
%timeit slow_df['a'].nlargest()

fast_df = pd.DataFrame({'a': np.arange(N)})
%timeit fast_df['a'].nlargest()

results in

448 ms ± 9.63 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
105 µs ± 850 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

so 4000 times slower with a non unique index. This was very surprising.

Installed Versions

INSTALLED VERSIONS

commit : e86ed37
python : 3.11.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.6.0
Version : Darwin Kernel Version 20.6.0: Fri Dec 16 00:34:59 PST 2022; root:xnu-7195.141.49~1/RELEASE_ARM64_T8101
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.1.1
numpy : 1.26.0
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 68.0.0
pip : 23.2.1
Cython : None
pytest : 7.4.2
hypothesis : None
sphinx : 7.2.6
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.16.1
pandas_datareader : None
bs4 : 4.12.2
bottleneck : None
dataframe-api-compat: None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.8.0
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : 11.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.11.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

Prior Performance

No response