dask.dataframe.tseries.resample.Resampler.sem
dask.dataframe.tseries.resample.Resampler.sem¶
- Resampler.sem()[source]¶
Compute standard error of the mean of groups, excluding missing values.
This docstring was copied from pandas.core.resample.Resampler.sem.
Some inconsistencies with the Dask version may exist.
For multiple groupings, the result index will be a MultiIndex.
- Parameters
- ddofint, default 1 (Not supported in Dask)
Degrees of freedom.
- numeric_onlybool, default False (Not supported in Dask)
Include only float, int or boolean data.
New in version 1.5.0.
Changed in version 2.0.0: numeric_only now defaults to
False.
- Returns
- Series or DataFrame
Standard error of the mean of values within each group.
Examples
For SeriesGroupBy:
>>> lst = ['a', 'a', 'b', 'b'] >>> ser = pd.Series([5, 10, 8, 14], index=lst) >>> ser a 5 a 10 b 8 b 14 dtype: int64 >>> ser.groupby(level=0).sem() a 2.5 b 3.0 dtype: float64
For DataFrameGroupBy:
>>> data = [[1, 12, 11], [1, 15, 2], [2, 5, 8], [2, 6, 12]] >>> df = pd.DataFrame(data, columns=["a", "b", "c"], ... index=["tuna", "salmon", "catfish", "goldfish"]) >>> df a b c tuna 1 12 11 salmon 1 15 2 catfish 2 5 8 goldfish 2 6 12 >>> df.groupby("a").sem() b c a 1 1.5 4.5 2 0.5 2.0
For Resampler:
>>> ser = pd.Series([1, 3, 2, 4, 3, 8], ... index=pd.DatetimeIndex(['2023-01-01', ... '2023-01-10', ... '2023-01-15', ... '2023-02-01', ... '2023-02-10', ... '2023-02-15'])) >>> ser.resample('MS').sem() 2023-01-01 0.577350 2023-02-01 1.527525 Freq: MS, dtype: float64