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 (int, optional) – Axis along which to search. If None, the function must return the index of the minimum value of the flattened array. 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.

  • keepdims (bool) – If True, the reduced axes (dimensions) must be included in the result as singleton dimensions, and, accordingly, the result must be compatible with the input array. Otherwise, if False, the reduced axes (dimensions) must not be included in the result. 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 Data types.

See also

dpnp.nanargmax

Returns the indices of the maximum values along an axis, igonring 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])