numpy.nanstd#

numpy.nanstd(a,axis=None,dtype=None,out=None,ddof=0,keepdims=<novalue>,*,where=<novalue>,mean=<novalue>,correction=<novalue>)[source]#

Compute the standard deviation along the specified axis, whileignoring NaNs.

Returns the standard deviation, a measure of the spread of adistribution, of the non-NaN array elements. The standard deviation iscomputed for the flattened array by default, otherwise over thespecified axis.

For all-NaN slices or slices with zero degrees of freedom, NaN isreturned and aRuntimeWarning is raised.

Parameters:
aarray_like

Calculate the standard deviation of the non-NaN values.

axis{int, tuple of int, None}, optional

Axis or axes along which the standard deviation is computed. The default isto compute the standard deviation of the flattened array.

dtypedtype, optional

Type to use in computing the standard deviation. For arrays ofinteger type the default is float64, for arrays of float types itis the same as the array type.

outndarray, optional

Alternative output array in which to place the result. It must havethe same shape as the expected output but the type (of thecalculated values) will be cast if necessary.

ddof{int, float}, optional

Means Delta Degrees of Freedom. The divisor used in calculationsisN-ddof, whereN represents the number of non-NaNelements. By defaultddof is zero.

keepdimsbool, optional

If this is set to True, the axes which are reduced are leftin the result as dimensions with size one. With this option,the result will broadcast correctly against the originala.

If this value is anything but the default it is passed throughas-is to the relevant functions of the sub-classes. If thesefunctions do not have akeepdims kwarg, a RuntimeError willbe raised.

wherearray_like of bool, optional

Elements to include in the standard deviation.Seereduce for details.

New in version 1.22.0.

meanarray_like, optional

Provide the mean to prevent its recalculation. The mean should havea shape as if it was calculated withkeepdims=True.The axis for the calculation of the mean should be the same as used inthe call to this std function.

New in version 2.0.0.

correction{int, float}, optional

Array API compatible name for theddof parameter. Only one of themcan be provided at the same time.

New in version 2.0.0.

Returns:
standard_deviationndarray, see dtype parameter above.

Ifout is None, return a new array containing the standarddeviation, otherwise return a reference to the output array. Ifddof is >= the number of non-NaN elements in a slice or the slicecontains only NaNs, then the result for that slice is NaN.

Notes

The standard deviation is the square root of the average of the squareddeviations from the mean:std=sqrt(mean(abs(x-x.mean())**2)).

The average squared deviation is normally calculated asx.sum()/N, whereN=len(x). If, however,ddof isspecified, the divisorN-ddof is used instead. In standardstatistical practice,ddof=1 provides an unbiased estimator of thevariance of the infinite population.ddof=0 provides a maximumlikelihood estimate of the variance for normally distributed variables.The standard deviation computed in this function is the square root ofthe estimated variance, so even withddof=1, it will not be anunbiased estimate of the standard deviation per se.

Note that, for complex numbers,std takes the absolute value beforesquaring, so that the result is always real and nonnegative.

For floating-point input, thestd is computed using the sameprecision the input has. Depending on the input data, this can causethe results to be inaccurate, especially for float32 (see examplebelow). Specifying a higher-accuracy accumulator using thedtypekeyword can alleviate this issue.

Examples

>>>importnumpyasnp>>>a=np.array([[1,np.nan],[3,4]])>>>np.nanstd(a)1.247219128924647>>>np.nanstd(a,axis=0)array([1., 0.])>>>np.nanstd(a,axis=1)array([0.,  0.5]) # may vary
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