pandas.Series.std¶
- Series.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)}
- 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 scalar.
- 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.
- Returns
- 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
Alternatively, ddof=0 can be set to normalize by N instead of N-1:
>>> df.std(ddof=0) age 16.269219 height 0.205609