dpnp.min

dpnp.min(a, axis=None, out=None, keepdims=False, initial=None, where=True)[source]

Return the minimum of an array or maximum along an axis.

For full documentation refer to numpy.min.

Parameters:

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

  • axis (int or tuple of ints, optional) – Axis or axes along which minimum values must be computed. By default, the minimum value must be computed over the entire array. If a tuple of integers, minimum values must be computed over multiple axes. 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, optional) – 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 the minimum value was computed over the entire array, a zero-dimensional array containing the minimum value; otherwise, a non-zero-dimensional array containing the minimum values. The returned array must have the same data type as a.

Return type:

dpnp.ndarray

Limitations

Parameters where, and initial are only supported with their default values. Otherwise NotImplementedError exception will be raised.

See also

dpnp.max

Return the maximum of an array.

dpnp.minimum

Element-wise minimum of two arrays, propagates NaNs.

dpnp.fmin

Element-wise minimum of two arrays, ignores NaNs.

dpnp.amin

The minimum value of an array along a given axis, propagates NaNs.

dpnp.nanmin

The minimum value of an array along a given axis, ignores NaNs.

Examples

>>> import dpnp as np
>>> a = np.arange(4).reshape((2,2))
>>> a
array([[0, 1],
       [2, 3]])
>>> np.min(a)
array(0)

>>> np.min(a, axis=0)   # Minima along the first axis
array([0, 1])
>>> np.min(a, axis=1)   # Minima along the second axis
array([0, 2])

>>> b = np.arange(5, dtype=float)
>>> b[2] = np.NaN
>>> np.min(b)
array(nan)