nums.numpy.min

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

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

This docstring was copied from numpy.min.

Some inconsistencies with the NumS version may exist.

Parameters

  • a (BlockArray) – Input data.

  • axis (None or int or tuple of ints, optional) – Axis or axes along which to operate. By default, flattened input is used. If this is a tuple of ints, the minimum is selected over multiple axes, instead of a single axis or all the axes as before.

  • out (BlockArray, optional) – Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output.

  • 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 input array. If the default value is passed, then keepdims will not be passed through to the amin method of sub-classes of BlockArray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.

  • initial (scalar, optional) – The maximum value of an output element. Must be present to allow computation on empty slice. See ~numpy.ufunc.reduce for details.

  • where (BlockArray of bool, optional) – Elements to compare for the minimum. See ~numpy.ufunc.reduce for details.

Returns

amin – Minimum of a. If axis is None, the result is a scalar value. If axis is given, the result is an array of dimension a.ndim - 1.

Return type

BlockArray

See also

amax

The maximum value of an array along a given axis, propagating any NaNs.

nanmin

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

minimum

Element-wise minimum of two arrays, propagating any NaNs.

fmin

Element-wise minimum of two arrays, ignoring any NaNs.

argmin

Return the indices of the minimum values.

nanmax, maximum, fmax

Notes

NaN values are propagated, that is if at least one item is NaN, the corresponding min value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmin.

Don’t use amin for element-wise comparison of 2 arrays; when a.shape[0] is 2, minimum(a[0], a[1]) is faster than amin(a, axis=0).

‘initial’ is currently not supported.

‘where’ is currently not supported.

‘out’ is currently not supported.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> a = nps.arange(4).reshape((2,2))  
>>> a.get()  
array([[0, 1],
       [2, 3]])
>>> nps.amin(a).get()           # Minimum of the flattened array  
array(0)
>>> nps.amin(a, axis=0).get()   # Minima along the first axis  
array([0, 1])
>>> nps.amin(a, axis=1).get()   # Minima along the second axis  
array([0, 2])

>>> nps.nanmin(b).get()  
0.0

previous

nums.numpy.copy

next

nums.numpy.max