pandas.core.groupby.SeriesGroupBy.std#

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

Compute standard deviation of groups, excluding missing values.

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

Parameters:
ddofint, default 1

Degrees of freedom.

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.

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