BUG: Unexpected cast to float for `DataFrame.groupby().agg(engine="numba")`

[willsthompson]

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Reproducible Example

import numpy as np
import pandas as pd
import numba as nb
from numba import njit

@njit(nb.int64(nb.int64[:], nb.int64[:]))
def return_one(values, index):
    return np.int64(1)

df = pd.DataFrame({"group": [1, 1, 2, 2, 2], "y": [1, 1, 2, 3, 2]})

noagg = return_one(df["y"].values, df.index.values)
aggs1 = df.groupby("group").agg(lambda s: return_one(s.values, s.index.values))
aggs2 = df.groupby("group").agg(return_one, engine="numba")

print(type(noagg))  # int
print(aggs1["y"].dtype)  # np.int64
print(aggs2["y"].dtype)  # np.float64

Issue Description

When executing the numba-compiled function standalone or with groupby.agg() without the numba engine, ints are returned as expected. However, when using Pandas' numba-compiled agg function, ints are coerced into floats.

Best I can tell the issue is here

pandas/pandas/core/groupby/numba_.py

Line 114 in b162331

result = np.empty((num_groups, num_columns))

, which seems to always initialize the array to float. When I add a dtype=values.dtype to that constructor, it works as expected.

Expected Behavior

Ideally the numba engine behavior would match Pandas' and use the dtype of the aggregate function's result, but I'm not sure that's possible. A more practical solution would be to accept an optional result_dtype argument that defaults to np.float (making that default more explicit) and pass that to the result array constructor. It would also be helpful for this nuance to be documented.

Installed Versions

INSTALLED VERSIONS

commit : 2e218d1
python : 3.11.9.final.0
python-bits : 64
OS : Darwin
OS-release : 23.4.0
Version : Darwin Kernel Version 23.4.0: Fri Mar 15 00:10:42 PDT 2024; root:xnu-10063.101.17~1/RELEASE_ARM64_T6000
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.5.3
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.8.2
setuptools : 65.5.0
pip : 24.0
Cython : 3.0.10
pytest : 7.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 5.2.1
html5lib : None
pymysql : None
psycopg2 : 2.9.3
jinja2 : 3.1.3
IPython : None
pandas_datareader: None
bs4 : 4.12.3
bottleneck : None
brotli : None
fastparquet : None
fsspec : 2024.3.1
gcsfs : None
matplotlib : 3.8.4
numba : 0.59.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 16.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.13.0
snappy : None
sqlalchemy : 2.0.30
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : 2024.1

[rhshadrach]

#35759 (comment)

cc @mroeschke