xorbits.pandas.Series.std#

Series.std(axis=None, skipna=True, level=None, ddof=1, combine_size=None, method=None)#

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)}) – For Series this parameter is unused and defaults to 0.

  • skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.

  • ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

  • numeric_only (bool, default False (Not supported yet)) – Include only float, int, boolean columns. Not implemented for Series.

Return type

scalar or Series (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
dtype: float64

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

>>> df.std(ddof=0)  
age       16.269219
height     0.205609
dtype: float64

This docstring was copied from pandas.core.series.Series.