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Reproducible Example

timestamps = [pd.Timestamp("2020-01-01") + i * pd.Timedelta(minutes=1) for i in range(200)]
dfs = []
for i in range(10):
    df_i = pd.DataFrame.from_dict(
        {
            "Timestamp": timestamps, 
            "groupby_value": [i * 0.01] * len(timestamps),
            "value": [i + 2] * len(timestamps)
        }
    )
    dfs += [df_i]
df = pd.concat(dfs)
df["Timestamp"] = pd.to_datetime(df["Timestamp"])

df_grouped = df.set_index("Timestamp").groupby("groupby_value").resample("1H").mean()

print(sorted(list(set(df["groupby_value"]))), "\n", sorted(list(set(df_grouped["groupby_value"]))))
""" Output:
[0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09] 
 [0.0, 0.01, 0.02, 0.029999999999999995, 0.03, 0.04, 0.05, 0.05999999999999999, 0.06, 0.07, 0.08, 0.09] """
print(df_grouped.reset_index(drop=True).groupby("groupby_value").count())
""" Output:
               value
groupby_value       
0.00               4
0.01               4
0.02               4
0.03               3
0.03               1  # <- another 0.03?
0.04               4
0.05               4
0.06               3
0.06               1  # <- another 0.06?
0.07               4
0.08               4
0.09               4
"""

Issue Description

After groupby and resample, where the groupby column has float values, new values appear in that column, which are close but not equal to some of the existing values.
For instance, in the example above we get 0.029999999999999995 and 0.03 instead of 0.03.
This leads to incorrect aggregations as well (see the last output in the example).

Expected Behavior

Expected: keep original values of floats used for grouping.

Installed Versions

INSTALLED VERSIONS

commit : 3fa869e
python : 3.10.9.final.0
python-bits : 64
OS : Darwin
OS-release : 22.2.0
Version : Darwin Kernel Version 22.2.0: Fri Nov 11 02:08:47 PST 2022; root:xnu-8792.61.2~4/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.0.0.dev0+1265.g3fa869ef90
numpy : 1.24.1
pytz : 2022.7.1
dateutil : 2.8.2
setuptools : 65.6.3
pip : 22.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : None
qtpy : None
pyqt5 : None