numpy.amin(a, axis=None, out=None, keepdims=<class 'numpy._globals._NoValue'>)
[source]
Return the minimum of an array or minimum along an axis.
Parameters: |
a : array_like Input data. axis : None or int or tuple of ints, optional Axis or axes along which to operate. By default, flattened input is used. New in version 1.7.0. 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 : ndarray, optional Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. See 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 |
---|---|
Returns: |
amin : ndarray or scalar Minimum of |
See also
amax
nanmin
minimum
fmin
argmin
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)
.
>>> a = np.arange(4).reshape((2,2)) >>> a array([[0, 1], [2, 3]]) >>> np.amin(a) # Minimum of the flattened array 0 >>> np.amin(a, axis=0) # Minima along the first axis array([0, 1]) >>> np.amin(a, axis=1) # Minima along the second axis array([0, 2])
>>> b = np.arange(5, dtype=float) >>> b[2] = np.NaN >>> np.amin(b) nan >>> np.nanmin(b) 0.0
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https://docs.scipy.org/doc/numpy-1.14.2/reference/generated/numpy.amin.html