# dask.array.nanstd

dask.array.nanstd(a, axis=None, dtype=None, keepdims=False, ddof=0, split_every=None, out=None)

  Compute the standard deviation along the specified axis, while ignoring NaNs.

  This docstring was copied from numpy.nanstd.

  Some inconsistencies with the Dask version may exist.

  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.

  Parameters:
    a array_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.

    dtype dtype, 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.

    out ndarray, 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.

    ddof{int, float}, optional

      Means Delta Degrees of Freedom. The divisor used in calculations is `N - ddof`, where `N` represents the number of non-NaN elements. By default ddof is zero.

    keepdims bool, 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.

    where array_like of bool, optional (Not supported in Dask)

      Elements to include in the standard deviation. See ~numpy.ufunc.reduce for details.

      Added in version 1.22.0.

    mean array_like, optional (Not supported in Dask)

      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.

      Added in version 2.0.0.

    correction{int, float}, optional (Not supported in Dask)

      Array API compatible name for the `ddof` parameter. Only one of them can be provided at the same time.

      Added in version 2.0.0.

  Returns:
    standard_deviation ndarray, 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.

  See also

  `var`, `mean`, `std`
  `nanvar`, `nanmean`
  ufuncs-output-type

  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`, where `N = len(x)`. If, however, ddof is specified, the divisor `N - 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 with `ddof=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

      >>> import numpy as np
      >>> 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