pandas 1.4.2

ParametersReturnsBackRef
nansem(values: 'np.ndarray', *, axis: 'int | None' = None, skipna: 'bool' = True, ddof: 'int' = 1, mask: 'npt.NDArray[np.bool_] | None' = None) -> 'float'

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.

Compute the standard error in the mean along given axis while ignoring NaNs

Examples

This example is valid syntax, but we were not able to check execution
>>> import pandas.core.nanops as nanops
... s = pd.Series([1, np.nan, 2, 3])
... nanops.nansem(s) 0.5773502691896258
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.nanops.nansem

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File: /pandas/core/nanops.py#971
type: <class 'function'>
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