bigframes.pandas.api.typing.SeriesGroupBy.std#

SeriesGroupBy.std(*args, **kwargs) → Series[source]#

Compute standard deviation of groups, excluding missing values.

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

Examples:

For SeriesGroupBy:

>>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
>>> ser = bpd.Series([7, 2, 8, 4, 3, 3], index=lst)
>>> ser.groupby(level=0).std()
a     3.21455
b     0.57735
dtype: Float64

For DataFrameGroupBy:

>>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
>>> df = bpd.DataFrame(data, index=['dog', 'dog', 'dog',
...                    'mouse', 'mouse', 'mouse', 'mouse'])
>>> df.groupby(level=0).std()
              a         b
dog         2.0  3.511885
mouse  2.217356       1.5

[2 rows x 2 columns]

Parameters:

numeric_only (bool, default False) Include only float, int or boolean data.

Returns:

Standard deviation of values within each group.

Return type:

bigframes.pandas.DataFrame or bigframes.pandas.Series