Description
Pandas version checks
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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I have confirmed this bug exists on the master branch of pandas.
Reproducible Example
In [1]: import pandas as pd
In [3]: s = pd.Series([1,2,3,3,4])
...: s.nlargest(2, keep='all')
Out[3]:
4 4
2 3
3 3
dtype: int64
In [4]: s = pd.Series([1,2,3,3,4])
...: s.nlargest(3, keep='all')
Out[4]:
4 4
2 3
3 3
dtype: int64
In [5]: pd.__version__
Out[5]: '1.4.0.dev0+1453.ga3c0e7bcfb
Issue Description
nlargest = 2 and nlargest = 3 gives the same output here with keep='all'
Expected Behavior
I'd expect s.nlargest(3, keep='all')
to return
4 4
2 3
3 3
1 2
dtype: int64
Installed Versions
pandas : 1.4.0.dev0+1453.ga3c0e7bcfb
numpy : 1.21.2
pytz : 2021.3
dateutil : 2.8.2
pip : 21.3
setuptools : 58.2.0
Cython : 0.29.24
pytest : 6.2.5
hypothesis : 6.23.2
sphinx : 4.2.0
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.2
IPython : 7.28.0
pandas_datareader: None
bs4 : 4.10.0
bottleneck : 1.3.2
fsspec : 2021.10.0
fastparquet : 0.7.1
gcsfs : 2021.10.0
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : 2021.10.0
scipy : 1.7.1
sqlalchemy : 1.4.25
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1