parameters: (a, axis=0, ddof=1)
return_statement: return s
docstring: Calculates the standard error of the mean of the input array.
  
      Also sometimes called standard error of measurement.
  
      Parameters
      ----------
      a : array_like
          An array containing the values for which the standard error is
          returned.
      axis : int 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.
      ddof : int, 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
      -------
      s : ndarray 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
      `stats.sem` as well as with the most common definition used (like in the R
      documentation).
  
      Examples
      --------
      Find standard error along the first axis:
  
      >>> 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
docstring_summary: Calculates the standard error of the mean of the input array.
docstring_tokens: Calculates the standard error of the mean of the input array .
function: def 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
      ----------
      a : array_like
          An array containing the values for which the standard error is
          returned.
      axis : int 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.
      ddof : int, 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
      -------
      s : ndarray 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
      `stats.sem` as well as with the most common definition used (like in the R
      documentation).
  
      Examples
      --------
      Find standard error along the first axis:
  
      >>> 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
  
      """
      a, axis = _chk_asarray(a, axis)
      n = a.count(axis=axis)
      s = a.std(axis=axis, ddof=ddof) / ma.sqrt(n)
      return s
function_tokens: def sem ( a , axis = 0 , ddof = 1 ) : a , axis = _chk_asarray ( a , axis ) n = a . count ( axis = axis ) s = a . std ( axis = axis , ddof = ddof ) / ma . sqrt ( n ) return s