numpy.amin¶

numpy.
amin
(a, axis=None, out=None, keepdims=<no value>, initial=<no value>)[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
doc.ufuncs
(Section “Output arguments”) for more details. 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 subclasses ofndarray
, however any nondefault value will be. If the subclass’ 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
reduce
for details.New in version 1.15.0.
Returns:  amin : ndarray or scalar
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
.
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
 Elementwise minimum of two arrays, propagating any NaNs.
fmin
 Elementwise minimum of two arrays, ignoring any NaNs.
argmin
 Return the indices of the minimum values.
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 elementwise comparison of 2 arrays; whena.shape[0]
is 2,minimum(a[0], a[1])
is faster thanamin(a, axis=0)
.Examples
>>> 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
>>> np.min([[50], [10]], axis=1, initial=0) array([50, 0])
Notice that the initial value is used as one of the elements for which the minimum is determined, unlike for the default argument Python’s max function, which is only used for empty iterables.
Notice that this isn’t the same as Python’s
default
argument.>>> np.min([6], initial=5) 5 >>> min([6], default=5) 6