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# scipy.stats.mstats.variation

scipy.stats.mstats.variation(a, axis=0, ddof=0)

  Compute the coefficient of variation.

  The coefficient of variation is the standard deviation divided by the mean. This function is equivalent to:

      np.std(x, axis=axis, ddof=ddof) / np.mean(x)

  The default for `ddof` is 0, but many definitions of the coefficient of variation use the square root of the unbiased sample variance for the sample standard deviation, which corresponds to `ddof=1`.

  Parameters:
    a array_like

      Input array.

    axis int or None, optional

      Axis along which to calculate the coefficient of variation. Default is 0. If None, compute over the whole array a.

    ddof int, optional

      Delta degrees of freedom. Default is 0.

  Returns:
    variation ndarray

      The calculated variation along the requested axis.

  Notes

  For more details about `variation`, see `scipy.stats.variation`.

  Examples

      >>> import numpy as np
      >>> from scipy.stats.mstats import variation
      >>> a = np.array([2,8,4])
      >>> variation(a)
      0.5345224838248487
      >>> b = np.array([2,8,3,4])
      >>> c = np.ma.masked_array(b, mask=[0,0,1,0])
      >>> variation(c)
      0.5345224838248487

  In the example above, it can be seen that this works the same as `scipy.stats.variation` except ‘stats.mstats.variation’ ignores masked array elements.