pandas.Series.std — pandas 3.0.0rc0+52.gb43b95d2b4 documentation (original) (raw)

Series.std(*, axis=None, skipna=True, ddof=1, numeric_only=False, **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)}

For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.std with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).

skipnabool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

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 False

Include only float, int, boolean columns. Not implemented for Series.

**kwargs

Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

scalar or Series (if level specified)

Standard deviation over requested axis.

See also

numpy.std

Compute the standard deviation along the specified axis.

Series.var

Return unbiased variance over requested axis.

Series.sem

Return unbiased standard error of the mean over requested axis.

Series.mean

Return the mean of the values over the requested axis.

Series.median

Return the median of the values over the requested axis.

Series.mode

Return the mode(s) of the Series.

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