BUG: DataFrame.rank() wrong result for inf

[auderson]

• 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
df = pd.DataFrame(np.random.randn(3, 4))
df.iloc[0, 0] = np.inf

df
          0         1         2         3
0       inf -0.614812 -0.316211 -0.049311
1  1.254238 -0.082490 -0.199442 -0.373851
2 -1.440219  0.723572  0.028726 -0.904849

df.rank()
     0    1    2    3
0  NaN  1.0  1.0  3.0
1  2.0  2.0  2.0  2.0
2  1.0  3.0  3.0  1.0

df.iloc[0, 0] = -np.inf

df.rank()
     0    1    2    3
0  1.0  1.0  1.0  3.0
1  3.0  2.0  2.0  2.0
2  2.0  3.0  3.0  1.0

Problem description

as you can see, -inf and inf get different results.

Expected Output

Output of pd.show_versions()

INSTALLED VERSIONS

commit : 3e89b4c
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-118-generic
Version : #119-Ubuntu SMP Tue Sep 8 12:30:01 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.18.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : 0.10.1
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: 0.9.0
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0

[vangorade]

Thanks @auderson for reporting issue.

master gives the expected output

>>> pd.__version__
'1.3.0.dev0+361.g23f2eb14c'   

>>> df = pd.DataFrame(np.random.randn(3, 4))
>>> df.iloc[0, 0] = np.inf

>>> df
          0         1         2         3
0       inf  1.644946  1.047380  1.661329
1  0.046478 -1.447429  0.137414  0.998330
2 -0.474645  0.735952 -0.234013 -0.230222

>>> df.rank()
     0    1    2    3
0  3.0  3.0  3.0  3.0
1  2.0  1.0  2.0  2.0
2  1.0  2.0  1.0  1.0

>>> df.iloc[0, 0] = -np.inf

>>>  df.rank()
     0    1    2    3
0  1.0  3.0  3.0  3.0
1  3.0  1.0  2.0  2.0
2  2.0  2.0  1.0  1.0

[jreback]

was fixed by #38681