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
axis : int, optional
dtype : dtype, optional
out : ndarray, optional
ddof : int, optional


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

See also
Notes
The standard deviation is the square root of the average of the squared deviations from the mean, i.e., std = 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. In standard statistical practice, ddof=1 provides an unbiased estimator of the variance of the infinite population. ddof=0 provides a maximum likelihood estimate of the variance for normally distributed variables. The standard deviation computed in this function is the square root of the estimated variance, so even with ddof=1, it will not be an unbiased estimate of the standard deviation per se.
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])