xorbits.pandas.groupby.DataFrameGroupBy.sem#

DataFrameGroupBy.sem(**kw)#

Compute standard error of the mean of groups, excluding missing values.

For multiple groupings, the result index will be a MultiIndex.

Parameters

  • ddof (int, default 1 (Not supported yet)) – Degrees of freedom.

  • numeric_only (bool, default False (Not supported yet)) –

    Include only float, int or boolean data.

    New in version 1.5.0(pandas).

    Changed in version 2.0.0(pandas): numeric_only now defaults to False.

Returns

Standard error of the mean of values within each group.

Return type

Series or DataFrame

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

This docstring was copied from pandas.core.groupby.generic.DataFrameGroupBy.