@@ -12116,7 +12116,6 @@ def std(
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) -> Series | Any : ...
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@deprecate_nonkeyword_arguments (version = "3.0" , allowed_args = ["self" ], name = "std" )
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- @doc (make_doc ("std" , ndim = 2 ))
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def std (
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self ,
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axis : Axis | None = 0 ,
@@ -12125,6 +12124,81 @@ def std(
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numeric_only : bool = False ,
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** kwargs ,
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) -> Series | Any :
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+ """
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+ Return sample standard deviation over requested axis.
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+
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+ Normalized by N-1 by default. This can be changed using the ddof argument.
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+
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+ Parameters
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+ ----------
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+ axis : {index (0), columns (1)}
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+ For `Series` this parameter is unused and defaults to 0.
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+
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+ .. warning::
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+
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+ The behavior of DataFrame.std with ``axis=None`` is deprecated,
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+ in a future version this will reduce over both axes and return a scalar
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+ To retain the old behavior, pass axis=0 (or do not pass axis).
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+
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+ skipna : bool, default True
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+ Exclude NA/null values. If an entire row/column is NA, the result
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+ will be NA.
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+ ddof : int, default 1
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+ Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
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+ where N represents the number of elements.
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+ numeric_only : bool, default False
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+ Include only float, int, boolean columns. Not implemented for Series.
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+ **kwargs : dict
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+ Parameters to accommodate numpy arguments.
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+
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+ Returns
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+ -------
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+ Series or DataFrame (if level specified)
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+ Standard deviation over requested axis.
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+
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+ See Also
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+ --------
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+ DataFrame.mean : Return the mean of the values over the requested axis.
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+ DataFrame.mediam : Return the mediam of the values over the requested axis.
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+ DataFrame.mode : Get the mode(s) of each element along the requested axis.
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+ DataFrame.sum : Return the sum of the values over the requested axis.
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+
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+ Notes
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+ -----
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+ To have the same behaviour as `numpy.std`, use `ddof=0` (instead of the
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+ default `ddof=1`)
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+
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+ Examples
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+ --------
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+ >>> df = pd.DataFrame(
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+ ... {
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+ ... "person_id": [0, 1, 2, 3],
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+ ... "age": [21, 25, 62, 43],
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+ ... "height": [1.61, 1.87, 1.49, 2.01],
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+ ... }
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+ ... ).set_index("person_id")
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+ >>> df
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+ age height
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+ person_id
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+ 0 21 1.61
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+ 1 25 1.87
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+ 2 62 1.49
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+ 3 43 2.01
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+
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+ The standard deviation of the columns can be found as follows:
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+
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+ >>> df.std()
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+ age 18.786076
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+ height 0.237417
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+ dtype: float64
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+
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+ Alternatively, `ddof=0` can be set to normalize by N instead of N-1:
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+
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+ >>> df.std(ddof=0)
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+ age 16.269219
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+ height 0.205609
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+ dtype: float64
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+ """
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result = super ().std (
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axis = axis , skipna = skipna , ddof = ddof , numeric_only = numeric_only , ** kwargs
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)
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