Description
-
I have checked that this issue has not already been reported.
-
I have confirmed this bug exists on the latest version of pandas.
-
(optional) I have confirmed this bug exists on the master branch of pandas.
Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.
Code Sample, a copy-pastable example
>>> import pandas as pd
>>> import numpy as np
>>> values = [-np.inf, 0, np.inf, np.nan, 2, np.nan]
>>> s = pd.Series(values)
>>> s
0 -inf
1 0.0
2 inf
3 NaN
4 2.0
5 NaN
dtype: float64
>>> kwargs = {'method': 'dense', 'na_option': 'bottom', 'ascending': False, 'pct': False, 'numeric_only': False}
>>> s.rank(**kwargs)
0 4.0
1 3.0
2 1.0
3 5.0
4 2.0
5 5.0
dtype: float64
>>> pd.DataFrame({'a':s}).rank(**kwargs)
a
0 4.0
1 3.0
2 1.0
3 4.0
4 2.0
5 4.0
>>> Problem description
The results being returned by Series.rank & DataFrame.rank seem to be inconsistent.
Expected Output
Output of DataFrame.rank must be returned correctly(as is being done by Series.rank)
Output of pd.show_versions()
INSTALLED VERSIONS
commit : 5f648bf
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-27-generic
Version : #29~20.04.1-Ubuntu SMP Wed Aug 11 15:58:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 57.4.0
Cython : 0.29.24
pytest : 6.2.4
hypothesis : 6.17.2
sphinx : 4.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.27.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1