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Showing content from https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.std.html below:

pandas.DataFrame.std — pandas 2.3.1 documentation

DataFrame.std(axis=0, 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), 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

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