rank() throws TypeError: 'NoneType' object is not callable

[levmorgan]

Code Sample, a copy-pastable example if possible

import numpy as np
import pandas as pd

groups = np.array(["A", "B"])
years = np.arange(2015, 2018)
values = np.arange(1,3)

table = np.array([years.repeat(len(groups)), np.tile(groups, len(years)), np.tile(values, len(years))]).T

table = pd.DataFrame(table, columns = ["year", "group", "val"])

table["year"] = table["year"].astype(int)
table["val"] = table["val"].astype(int)
table["group"] = table["group"].astype(str)

grp = table.groupby("year")

grp.rank()

Problem description

Running the rank method on the grouped table throws TypeError: 'NoneType' object is not callable

Expected Output

It should return ranked data for each year, or a descriptive error message.

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-70-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.1
setuptools : 39.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.999999999
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None

[TomAugspurger]

Thanks for the excellent report. This appears to be "fixed" on master, as no error is raised.

In [2]: import numpy as np
   ...: import pandas as pd
   ...:
   ...: groups = np.array(["A", "B"])
   ...: years = np.arange(2015, 2018)
   ...: values = np.arange(1,3)
   ...:
   ...: table = np.array([years.repeat(len(groups)), np.tile(groups, len(years)), np.tile(values, len(years))]).T
   ...:
   ...: table = pd.DataFrame(table, columns = ["year", "group", "val"])
   ...:
   ...: table["year"] = table["year"].astype(int)
   ...: table["val"] = table["val"].astype(int)
   ...: table["group"] = table["group"].astype(str)
   ...:
   ...: grp = table.groupby("year")
   ...:
   ...: grp.rank()
Out[2]:
   val
0  1.0
1  2.0
2  1.0
3  2.0
4  1.0
5  2.0

We still have #19560 for discussing what to do with non-numeric data.