# jax.numpy.std#

jax.numpy.std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=None)[source]#

Compute the standard deviation along the specified axis.

LAX-backend implementation of `numpy.std()`.

Original docstring below.

Returns the standard deviation, a measure of the spread of a distribution, of the array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis.

Parameters:
• a (array_like) â€“ Calculate the standard deviation of these values.

• axis (None or int or tuple of ints, optional) â€“ Axis or axes along which the standard deviation is computed. The default is to compute the standard deviation of the flattened array.

• dtype (dtype, optional) â€“ Type to use in computing the standard deviation. For arrays of integer type the default is float64, for arrays of float types it is the same as the array type.

• ddof (int, optional) â€“ Means Delta Degrees of Freedom. The divisor used in calculations is `N - ddof`, where `N` represents the number of elements. By default ddof is zero.

• keepdims (bool, optional) â€“

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

If the default value is passed, then keepdims will not be passed through to the std method of sub-classes of ndarray, however any non-default value will be. If the sub-classâ€™ method does not implement keepdims any exceptions will be raised.

• where (array_like of bool, optional) â€“ Elements to include in the standard deviation. See ~numpy.ufunc.reduce for details.

• out (None) â€“

Returns:

standard_deviation â€“ If out is None, return a new array containing the standard deviation, otherwise return a reference to the output array.

Return type:

ndarray, see dtype parameter above.