numpy.argmin
- numpy.argmin(a, axis=None, out=None, *, keepdims=<no value>)[source]
-
Returns the indices of the minimum values along an axis.
- Parameters:
-
- aarray_like
-
Input array.
- axisint, optional
-
By default, the index is into the flattened array, otherwise along the specified axis.
- outarray, optional
-
If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype.
- keepdimsbool, 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 array.
New in version 1.22.0.
- Returns:
-
- index_arrayndarray of ints
-
Array of indices into the array. It has the same shape as
a.shape
with the dimension alongaxis
removed. Ifkeepdims
is set to True, then the size ofaxis
will be 1 with the resulting array having same shape asa.shape
.
See also
-
ndarray.argmin
,argmax
amin
-
The minimum value along a given axis.
unravel_index
-
Convert a flat index into an index tuple.
take_along_axis
-
Apply
np.expand_dims(index_array, axis)
from argmin to an array as if by calling min.
Notes
In case of multiple occurrences of the minimum values, the indices corresponding to the first occurrence are returned.
Examples
>>> a = np.arange(6).reshape(2,3) + 10 >>> a array([[10, 11, 12], [13, 14, 15]]) >>> np.argmin(a) 0 >>> np.argmin(a, axis=0) array([0, 0, 0]) >>> np.argmin(a, axis=1) array([0, 0])
Indices of the minimum elements of a N-dimensional array:
>>> ind = np.unravel_index(np.argmin(a, axis=None), a.shape) >>> ind (0, 0) >>> a[ind] 10
>>> b = np.arange(6) + 10 >>> b[4] = 10 >>> b array([10, 11, 12, 13, 10, 15]) >>> np.argmin(b) # Only the first occurrence is returned. 0
>>> x = np.array([[4,2,3], [1,0,3]]) >>> index_array = np.argmin(x, axis=-1) >>> # Same as np.amin(x, axis=-1, keepdims=True) >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1) array([[2], [0]]) >>> # Same as np.amax(x, axis=-1) >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), ... axis=-1).squeeze(axis=-1) array([2, 0])
Setting
keepdims
toTrue
,>>> x = np.arange(24).reshape((2, 3, 4)) >>> res = np.argmin(x, axis=1, keepdims=True) >>> res.shape (2, 1, 4)
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