Series.std(axis=None, skipna=True, ddof=1, split_every=False, dtype=None, out=None, numeric_only=None)

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)}
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 (Not supported in Dask)

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

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
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
```

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

```>>> df.std(ddof=0)
age       16.269219
height     0.205609
```