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# 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])

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