# scipy.stats.variation

scipy.stats.variation(a, axis=0, nan_policy='propagate', 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.

    nan_policy{‘propagate’, ‘raise’, ‘omit’}, optional

      Defines how to handle when input contains nan. The following options are available (default is ‘propagate’):

      - ‘propagate’: returns nan

      - ‘raise’: throws an error

      - ‘omit’: performs the calculations ignoring nan values

    ddof int, optional

      Delta degrees of freedom. Default is 0.

  Returns
    variation ndarray

      The calculated variation along the requested axis.

  References

  1

    Zwillinger, D. and Kokoska, S. (2000). CRC Standard Probability and Statistics Tables and Formulae. Chapman & Hall: New York. 2000.

  Examples

      >>> from scipy.stats import variation
      >>> variation([1, 2, 3, 4, 5])
      0.47140452079103173

scipy.stats.tsem scipy.stats.find_repeats