pandas.core.groupby.SeriesGroupBy.std#

SeriesGroupBy.std(ddof=1, engine=None, engine_kwargs=None, numeric_only=False, skipna=True)[source] #

Compute standard deviation of groups, excluding missing values.

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

Parameters:

ddofint, default 1

Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

enginestr, default None

'cython' : Runs the operation through C-extensions from cython.
'numba' : Runs the operation through JIT compiled code from numba.
None : Defaults to 'cython' or globally setting compute.use_numba

Added in version 1.4.0.

engine_kwargsdict, default None

For 'cython' engine, there are no accepted engine_kwargs
For 'numba' engine, the engine can accept nopython, nogil and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {{'nopython': True, 'nogil': False, 'parallel': False}}

Added in version 1.4.0.

numeric_onlybool, default False

Include only float, int or boolean data.

Added in version 1.5.0.

Changed in version 2.0.0: numeric_only now defaults to False.

skipnabool, default True

Exclude NA/null values. If an entire group is NA, the result will be NA.

Added in version 3.0.0.

Returns:

Series or DataFrame: Standard deviation of values within each group.

See also

Series.groupby: Apply a function groupby to a Series.
DataFrame.groupby: Apply a function groupby to each row or column of a DataFrame.

Examples

For SeriesGroupBy:

>>> lst = ["a", "a", "a", "b", "b", "b"]
>>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst)
>>> ser
a 7
a 2
a 8
b 4
b 3
b 3
dtype: int64
>>> 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 = pd.DataFrame(
...  data, index=["dog", "dog", "dog", "mouse", "mouse", "mouse", "mouse"]
... )
>>> df
 a b
 dog 1 1
 dog 3 4
 dog 5 8
mouse 7 4
mouse 7 4
mouse 8 2
mouse 3 1
>>> df.groupby(level=0).std()
 a b
dog 2.000000 3.511885
mouse 2.217356 1.500000

pandas.core.groupby.SeriesGroupBy.std#

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