std(self, axis=None, skipna=True, level=None, ddof=1, numeric_only=None, **kwargs)
Normalized by N-1 by default. This can be changed using the ddof argument.
To have the same behaviour as numpy.std
, use :None:None:`ddof=0` (instead of the default :None:None:`ddof=1`)
Exclude NA/null values. If an entire row/column is NA, the result will be NA.
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.
Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
Return sample standard deviation over requested axis.
>>> 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:
This example is valid syntax, but we were not able to check execution>>> df.std() age 18.786076 height 0.237417
Alternatively, :None:None:`ddof=0` can be set to normalize by N instead of N-1:
>>> df.std(ddof=0) age 16.269219 height 0.205609See :
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