nanstd

nanstd(
    a,
    axis=None,
    ddof=0,
    *,
    return_mean=False,
    ignore_inf=False,
    validate=True,
)

Return the standard deviation while skipping NaN values.

Parameters

a : array_like: Input data. Supported numeric inputs are normalized to a contiguous kernel array when validate=True. Computed reducers promote integer and bool inputs to float64; exact selection reducers keep integer and bool dtypes where the selected value can be returned exactly. Complex and object arrays are not supported.
axis : None, 0, -1, or int = None: Axis to reduce. None reduces the whole array. 0 reduces strided reducing-axis slices into the remaining shape. -1 and ndim - 1 reduce contiguous slices. Other axes raise NotImplementedError.
ddof : int = 0: Delta degrees of freedom. Results with nvalid <= ddof are NaN.
return_mean : bool = False: If True, return (standard_deviation, mean). For axis=None this reuses the mean already computed by the variance kernel.
ignore_inf : bool = False: If False, skip only NaN values and keep +/-inf values, matching NumPy’s nan* reducers. If True, skip all non-finite values.
validate : bool = True: If True, check dtype, dimensionality, contiguity, and axis validity before entering the Rust kernel. If False, the caller must provide a contiguous supported kernel dtype (float32, float64, bool, or a NumPy integer dtype). validate=False skips dtype promotion: integer and bool arrays are reduced directly, while complex and object arrays remain unsupported.

out : float or ndarray: Reduction result. axis=None returns a Python float. Axis reductions return an array with the reduced axis removed.

NaN-aware reducers skip NaN values directly, without building a filtered copy of the input.