# scipy.stats.tstd

scipy.stats.tstd(a, limits=None, inclusive=(True, True), axis=0, ddof=1)

  Compute the trimmed sample standard deviation.

  This function finds the sample standard deviation of given values, ignoring values outside the given limits.

  Parameters:
    a array_like

      Array of values.

    limits None or (lower limit, upper limit), optional

      Values in the input array less than the lower limit or greater than the upper limit will be ignored. When limits is None, then all values are used. Either of the limit values in the tuple can also be None representing a half-open interval. The default value is None.

    inclusive(bool, bool), optional

      A tuple consisting of the (lower flag, upper flag). These flags determine whether values exactly equal to the lower or upper limits are included. The default value is (True, True).

    axis int or None, optional

      Axis along which to operate. Default is 0. If None, compute over the whole array a.

    ddof int, optional

      Delta degrees of freedom. Default is 1.

  Returns:
    tstd float

      Trimmed sample standard deviation.

  Notes

  `tstd` computes the unbiased sample standard deviation, i.e. it uses a correction factor `n / (n - 1)`.

  Examples

      >>> import numpy as np
      >>> from scipy import stats
      >>> x = np.arange(20)
      >>> stats.tstd(x)
      5.9160797830996161
      >>> stats.tstd(x, (3,17))
      4.4721359549995796