numpy.nanstd#
- numpy.nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>, *, where=<no value>, mean=<no value>)[source]#
Compute the standard deviation along the specified axis, while ignoring NaNs.
Returns the standard deviation, a measure of the spread of a distribution, of the non-NaN array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis.
For all-NaN slices or slices with zero degrees of freedom, NaN is returned and a RuntimeWarning is raised.
New in version 1.8.0.
- 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 is to compute the standard deviation of the flattened array.
- dtypedtype, optional
Type to use in computing the standard deviation. For arrays of integer type the default is float64, for arrays of float types it is the same as the array type.
- outndarray, optional
Alternative output array in which to place the result. It must have the same shape as the expected output but the type (of the calculated values) will be cast if necessary.
- ddofint, optional
Means Delta Degrees of Freedom. The divisor used in calculations is
N - ddof
, whereN
represents the number of non-NaN elements. By default ddof is zero.- keepdimsbool, optional
If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original a.
If this value is anything but the default it is passed through as-is to the relevant functions of the sub-classes. If these functions do not have a keepdims kwarg, a RuntimeError will be raised.
- wherearray_like of bool, optional
Elements to include in the standard deviation. See
reduce
for details.New in version 1.22.0.
- meanarray like, optional
Provide the mean to prevent its recalculation. The mean should have a shape as if it was calculated with
keepdims=True
. The axis for the calculation of the mean should be the same as used in the call to this std function.New in version 1.26.0.
- Returns:
- standard_deviationndarray, see dtype parameter above.
If out is None, return a new array containing the standard deviation, otherwise return a reference to the output array. If ddof is >= the number of non-NaN elements in a slice or the slice contains only NaNs, then the result for that slice is NaN.
Notes
The standard deviation is the square root of the average of the squared deviations from the mean:
std = sqrt(mean(abs(x - x.mean())**2))
.The average squared deviation is normally calculated as
x.sum() / N
, whereN = len(x)
. If, however, ddof is specified, the divisorN - ddof
is used instead. In standard statistical practice,ddof=1
provides an unbiased estimator of the variance of the infinite population.ddof=0
provides a maximum likelihood estimate of the variance for normally distributed variables. The standard deviation computed in this function is the square root of the estimated variance, so even withddof=1
, it will not be an unbiased estimate of the standard deviation per se.Note that, for complex numbers,
std
takes the absolute value before squaring, so that the result is always real and nonnegative.For floating-point input, the std is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-accuracy accumulator using the
dtype
keyword can alleviate this issue.Examples
>>> 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