DataFrameGroupBy.aggregate engine='numba' doesn't pass index

[richbwood]

• 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.

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Code Sample, a copy-pastable example

df = pd.DataFrame({'group': ['A', 'A', 'B'], 'v': [4, 5, 6]}, index=[-1, -2, -3])

def f(values, index):
    return np.mean(index)
df.groupby('group').aggregate(f, engine='numba')

# Result is:
#          v
# group     
# A      0.5
# B      2.0

Problem description

The behaviour when engine='numba' is inconsistent with engine='cython':

def f_cython(x):
    return np.mean(x.index)
df.groupby('group').aggregate(f_cython, engine='cython')

# Result is:
#          v
# group     
# A      -1.5
# B      -3.0

Furthermore engine='numba' would be more useful if it calls func with the DataFrame index.

Expected Output

         v
group     
A      -1.5
B      -3.0

Output of pd.show_versions()

INSTALLED VERSIONS

commit : c7f7443
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-80-generic
Version : #90-Ubuntu SMP Fri Jul 9 22:49:44 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8

pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 19.2.3
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.2.4
hypothesis : 6.14.1
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.26.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.1.1
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1

[Navaneethan2503]

I would like to work on this issue , can you make assign to me @richbwood