code: def nansem(values, axis=None, skipna=True, ddof=1, mask=None):
      
  
      # This checks if non-numeric-like data is passed with numeric_only=False
      # and raises a TypeError otherwise
      nanvar(values, axis, skipna, ddof=ddof, mask=mask)
  
      if mask is None:
          mask = isna(values)
      if not is_float_dtype(values.dtype):
          values = values.astype('f8')
      count, _ = _get_counts_nanvar(mask, axis, ddof, values.dtype)
      var = nanvar(values, axis, skipna, ddof=ddof)
  
      return np.sqrt(var) / np.sqrt(count)
docs: Compute the standard error in the mean along given axis while ignoring NaNs
  
      Parameters
      ----------
      values : ndarray
      axis: int, optional
      skipna : bool, default True
      ddof : int, default 1
          Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
          where N represents the number of elements.
      mask : ndarray[bool], optional
          nan-mask if known
  
      Returns
      -------
      result : float64
          Unless input is a float array, in which case use the same
          precision as the input array.
  
      Examples
      --------
      >>> import pandas.core.nanops as nanops
      >>> s = pd.Series([1, np.nan, 2, 3])
      >>> nanops.nansem(s)
       0.5773502691896258