dask_expr._resample.Resampler.sem

dask_expr._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