- ak.nanstd(x, weight=None, ddof=0, axis=None, *, keepdims=False, mask_identity=True, highlevel=True, behavior=None, attrs=None)#
x – The data on which to compute the standard deviation (anything
weight – Data that can be broadcasted to
xto give each value a weight. Weighting values equally is the same as no weights; weighting some values higher increases the significance of those values. Weights can be zero or negative.
ddof (int) – “delta degrees of freedom”: the divisor used in the calculation is
sum(weights) - ddof. Use this for “reduced standard deviation.”
axis (None or int) – If None, combine all values from the array into a single scalar result; if an int, group by that axis:
0is the outermost,
1is the first level of nested lists, etc., and negative
axiscounts from the innermost:
-1is the innermost,
-2is the next level up, etc.
keepdims (bool) – If False, this function decreases the number of dimensions by 1; if True, the output values are wrapped in a new length-1 dimension so that the result of this operation may be broadcasted with the original array.
mask_identity (bool) – If True, the application of this function on empty lists results in None (an option type); otherwise, the calculation is followed through with the reducers’ identities, usually resulting in floating-point
attrs (None or dict) – Custom attributes for the output array, if high-level.
ak.std, but treating NaN (“not a number”) values as missing.
with all other arguments unchanged.