dask_expr._collection.Series.std
dask_expr._collection.Series.std¶
- Series.std(axis=0, skipna=True, ddof=1, numeric_only=False, split_every=False, **kwargs)¶
Return sample standard deviation over requested axis.
This docstring was copied from pandas.core.frame.DataFrame.std.
Some inconsistencies with the Dask version may exist.
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.
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.
- 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 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