ENH: Support "std" aggregation in groupby for groupby datetime column · Issue #48481 · pandas-dev/pandas (original) (raw)
Pandas version checks
- I have checked that this issue has not already been reported.
- I have confirmed this bug exists on the latest version of pandas.
- I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
df = pd.DataFrame( [ [1, "2022-02-02"], [1, "2022-03-02"], [1, "2022-03-02"], [2, "2022-04-02"], [2, "2022-04-02"], [2, "2022-05-02"], ], columns=["a", "dt"] ) df["dt"] = pd.to_datetime(df["dt"]) gb = df.groupby("a") print(gb["dt"].agg(lambda d: d.std())) print(gb["dt"].agg("std"))
Issue Description
Computing standard deviation fails on GroupBy object, but it is successful with the std
method on DataFrame.
Traceback (most recent call last):
File "example.py", line 16, in <module>
print(gb["dt"].agg("std"))
File "pandas/pandas/core/groupby/generic.py", line 276, in aggregate
return getattr(self, func)(*args, **kwargs)
File "pandas/pandas/core/groupby/groupby.py", line 2261, in std
result = self._get_cythonized_result(
File "pandas/pandas/core/groupby/groupby.py", line 3818, in _get_cythonized_result
res_mgr = mgr.grouped_reduce(blk_func, ignore_failures=True)
File "pandas/core/internals/base.py", line 199, in grouped_reduce
res = func(arr)
File "pandas/pandas/core/groupby/groupby.py", line 3778, in blk_func
vals = vals.astype(cython_dtype, copy=False)
File "pandas/pandas/core/arrays/datetimes.py", line 636, in astype
return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy)
File "pandas/pandas/core/arrays/datetimelike.py", line 507, in astype
raise TypeError(msg)
TypeError: Cannot cast DatetimeArray to dtype float64
Expected Behavior
Computing standard deviation on GroupBy should be successful.
Installed Versions
Tested on master and released version. Failed in both cases.
INSTALLED VERSIONS
commit : 047c11d
python : 3.10.4.final.0
python-bits : 64
OS : Darwin
OS-release : 21.6.0
Version : Darwin Kernel Version 21.6.0: Wed Aug 10 14:28:23 PDT 2022; root:xnu-8020.141.5~2/RELEASE_ARM64_T6000
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.6.0.dev0+89.g047c11d780
numpy : 1.22.4
pytz : 2022.1
dateutil : 2.8.2
setuptools : 61.2.0
pip : 22.1.2
Cython : None
pytest : None
hypothesis : None
sphinx : 5.1.1
blosc : None
feather : None
xlsxwriter : 3.0.3
lxml.etree : 4.9.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.4.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : 1.3.5
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.5.2
numba : 0.56.0
numexpr : None
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.8.1
snappy : None
sqlalchemy : 1.4.39
tables : None
tabulate : 0.8.10
xarray : None
xlrd : 2.0.1
xlwt : None
zstandard : None
tzdata : None