BPt.Dataset.std#

Dataset.std(axis=None, skipna=True, level=None, ddof=1, numeric_only=None, **kwargs)[source]#

Return sample standard deviation over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument.

Parameters

axis{index (0), columns (1)}: For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
levelint or level name, default None: If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.
ddofint, default 1: Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
numeric_onlybool, default None: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Deprecated since version 1.5.0: Specifying numeric_only=None is deprecated. The default value will be False in a future version of pandas.

Returns

Series or DataFrame (if level specified)

Notes

To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)

Examples

>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
...                   'age': [21, 25, 62, 43],
...                   'height': [1.61, 1.87, 1.49, 2.01]}
...                  ).set_index('person_id')
>>> df
           age  height
person_id
0           21    1.61
1           25    1.87
2           62    1.49
3           43    2.01

The standard deviation of the columns can be found as follows:

>>> df.std()
age       18.786076
height     0.237417

Alternatively, ddof=0 can be set to normalize by N instead of N-1:

>>> df.std(ddof=0)
age       16.269219
height     0.205609