ak.nanstd#

Defined in awkward.operations.ak_std on line 100.

ak.nanstd(x, weight=None, ddof=0, axis=None, *, keepdims=False, mask_identity=True, highlevel=True, behavior=None, attrs=None)#

Parameters:

x – The data on which to compute the standard deviation (anything ak.to_layout recognizes).
weight – Data that can be broadcasted to x to 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: 0 is the outermost, 1 is the first level of nested lists, etc., and negative axis counts from the innermost: -1 is the innermost, -2 is 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 nan.
highlevel (bool) – If True, return an ak.Array; otherwise, return a low-level ak.contents.Content subclass.
behavior (None or dict) – Custom ak.behavior for the output array, if high-level.
attrs (None or dict) – Custom attributes for the output array, if high-level.

Like ak.std, but treating NaN (“not a number”) values as missing.

Equivalent to

ak.std(ak.nan_to_none(array))

with all other arguments unchanged.