dpnp.nanargmin

dpnp.nanargmin(a, axis=None, out=None, *, keepdims=False)[source]

Returns the indices of the minimum values along an axis ignoring NaNs.

For full documentation refer to numpy.nanargmin.

Parameters:

  • a ({dpnp.ndarray, usm_ndarray}) -- Input array.

  • axis ({None, int}, optional) -- Axis along which to operate. By default flattened input is used. Default: None.

  • out ({None, dpnp.ndarray, usm_ndarray}, optional) -- If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype. Default: None.

  • keepdims ({None, 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 array. Default: False.

Returns:

out -- If axis is None, a zero-dimensional array containing the index of the first occurrence of the minimum value ignoring NaNs; otherwise, a non-zero-dimensional array containing the indices of the minimum values ignoring NaNs. The returned array must have the default array index data type. For all-NaN slices ValueError is raised. Warning: the results cannot be trusted if a slice contains only NaNs and Infs.

Return type:

dpnp.ndarray

Limitations

Input and output arrays are only supported as either dpnp.ndarray or dpctl.tensor.usm_ndarray. Input array data types are limited by supported DPNP Available array data types.

See also

dpnp.nanargmax

Returns the indices of the maximum values along an axis, ignoring NaNs.

dpnp.argmin

Returns the indices of the minimum values along an axis.

Examples

>>> import dpnp as np
>>> a = np.array([[np.nan, 4], [2, 3]])
>>> np.argmin(a)
array(0)
>>> np.nanargmin(a)
array(2)
>>> np.nanargmin(a, axis=0)
array([1, 1])
>>> np.nanargmin(a, axis=1)
array([1, 0])