@disallow('<STR_LIT>', '<STR_LIT>')<EOL>def nansem(values, axis=None, skipna=True, ddof=<NUM_LIT:1>, mask=None):
<EOL>nanvar(values, axis, skipna, ddof=ddof, mask=mask)<EOL>if mask is None:<EOL><INDENT>mask = isna(values)<EOL><DEDENT>if not is_float_dtype(values.dtype):<EOL><INDENT>values = values.astype('<STR_LIT>')<EOL><DEDENT>count, _ = _get_counts_nanvar(mask, axis, ddof, values.dtype)<EOL>var = nanvar(values, axis, skipna, ddof=ddof)<EOL>return np.sqrt(var) / np.sqrt(count)<EOL>
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