nansem(values: 'np.ndarray', *, axis: 'int | None' = None, skipna: 'bool' = True, ddof: 'int' = 1, mask: 'npt.NDArray[np.bool_] | None' = None) -> 'float'
Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
nan-mask if known
Unless input is a float array, in which case use the same precision as the input array.
Compute the standard error in the mean along given axis while ignoring NaNs
>>> import pandas.core.nanops as nanopsSee :
... s = pd.Series([1, np.nan, 2, 3])
... nanops.nansem(s) 0.5773502691896258
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pandas.core.nanops.nansem
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