pandas.core.groupby.DataFrameGroupBy.std — pandas 3.0.0.dev0+2098.g9c5b9ee823 documentation (original) (raw)

DataFrameGroupBy.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 settingcompute.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, nogiland parallel dictionary keys. The values must either be True orFalse. 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