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scipy.stats.mstats.

# sem

scipy.stats.mstats.sem(a, axis=0, ddof=1)

  Calculates the standard error of the mean of the input array.

  Also sometimes called standard error of measurement.

  Parameters:
    aarray_like

      An array containing the values for which the standard error is returned.

    axisint or None, optional

      If axis is None, ravel a first. If axis is an integer, this will be the axis over which to operate. Defaults to 0.

    ddofint, optional

      Delta degrees-of-freedom. How many degrees of freedom to adjust for bias in limited samples relative to the population estimate of variance. Defaults to 1.

  Returns:
    sndarray or float

      The standard error of the mean in the sample(s), along the input axis.

  Notes

  The default value for ddof changed in scipy 0.15.0 to be consistent with `scipy.stats.sem` as well as with the most common definition used (like in the R documentation).

  Examples

  Find standard error along the first axis:

      >>> import numpy as np
      >>> from scipy import stats
      >>> a = np.arange(20).reshape(5,4)
      >>> print(stats.mstats.sem(a))
      [2.8284271247461903 2.8284271247461903 2.8284271247461903
       2.8284271247461903]

  Find standard error across the whole array, using n degrees of freedom:

      >>> print(stats.mstats.sem(a, axis=None, ddof=0))
      1.2893796958227628