amin(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)
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)
.
Input data.
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.
Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. See ufuncs-output-type
for more details.
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 :None:None:`keepdims` will not be passed through to the amin
method of sub-classes of ndarray
, however any non-default value will be. If the sub-class' method does not implement :None:None:`keepdims` any exceptions will be raised.
The maximum value of an output element. Must be present to allow computation on empty slice. See :None:None:`~numpy.ufunc.reduce` for details.
Elements to compare for the minimum. See :None:None:`~numpy.ufunc.reduce` for details.
Minimum of a. If :None:None:`axis` is None, the result is a scalar value. If :None:None:`axis` is given, the result is an array of dimension a.ndim - 1
.
Return the minimum of an array or minimum along an axis.
amax
The maximum value of an array along a given axis, propagating any NaNs.
argmin
Return the indices of the minimum values.
fmin
Element-wise minimum of two arrays, ignoring any NaNs.
minimum
Element-wise minimum of two arrays, propagating any NaNs.
nanmin
The minimum value of an array along a given axis, ignoring any NaNs.
>>> 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])
>>> np.amin(a, where=[False, True], initial=10, axis=0) array([10, 1])
>>> b = np.arange(5, dtype=float)
... b[2] = np.NaN
... np.amin(b) nan
>>> np.amin(b, where=~np.isnan(b), initial=10) 0.0
>>> np.nanmin(b) 0.0
>>> np.amin([[-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.amin([6], initial=5) 5
>>> min([6], default=5) 6See :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.amin
dask.array.reductions.min
dask.array.reductions.partial_reduce
numpy.matrixlib.defmatrix.matrix.min
numpy.linalg.linalg._multi_svd_norm
dask.array.chunk.coarsen
numpy.ma.core.maximum
numpy.ma.core.minimum
numpy.amax
dask.array.random.RandomState.lognormal
numpy.nanmax
numpy.argmin
dask.array.core.Array.min
numpy.nanmin
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them