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Standard Error of the Mean works on Groupby objects, but not RollingGroupby objects.
Groupby object
data=pd.DataFrame({"a": np.linspace(0, 100, 101),
"b": np.random.random(101).round(1) # values we can group on
})
# print values to 2 decimal places. Just showing it works.data.groupby("b").sem().T.round(2)
Problem description
RollingGroupby object raises AttributeError
data=pd.DataFrame({"a": np.linspace(0, 100, 101),
"b": np.random.random(101).round(1) # values we can group on
})
# print values to 2 decimal places. Just showing it works.data.rolling(5).sem()
I believe API of Groupby and RollingGroupby are supposed to be as similar as possible. Than an optomized aggregation is available in one, but not the other is a problem.
Code Sample, a copy-pastable example if possible
Standard Error of the Mean works on
Groupby
objects, but notRollingGroupby
objects.Groupby
objectProblem description
RollingGroupby
object raisesAttributeError
I believe API of
Groupby
andRollingGroupby
are supposed to be as similar as possible. Than an optomized aggregation is available in one, but not the other is a problem.Expected Output
The output should be the equivalent of
where
ddof
is degrees of freedom.Output of
pd.show_versions()
pandas: 0.24.2
pytest: 4.4.1
pip: 19.0.3
setuptools: 41.0.0
Cython: None
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 2.0.1
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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