numpy.ma.std

numpy.ma.std(self, axis=None, dtype=None, out=None, ddof=0)

Compute the standard deviation along the specified axis.

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

Calculate the standard deviation of these values.

axis : int, optional

Axis along which the standard deviation is computed. The default is to compute the standard deviation of the flattened array.

dtype : dtype, optional

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.

out : ndarray, optional

Alternative output array in which to place the result. It must have the same shape as the expected output but the type (of the calculated values) will be cast if necessary.

ddof : int, optional

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 (biased estimate).

Returns:

standard_deviation : {ndarray, scalar}; see dtype parameter above.

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

See also

numpy.var

Variance

numpy.mean

Average

Notes

The standard deviation is the square root of the average of the squared deviations from the mean, i.e., var = sqrt(mean(abs(x - x.mean())**2)).

The mean is normally calculated as x.sum() / N, where N = len(x). If, however, ddof is specified, the divisor N - ddof is used instead.

Note that, for complex numbers, std takes the absolute value before squaring, so that the result is always real and nonnegative.

Examples

>>> a = np.array([[1, 2], [3, 4]])
>>> np.std(a)
1.1180339887498949
>>> np.std(a, 0)
array([ 1.,  1.])
>>> np.std(a, 1)
array([ 0.5,  0.5])