numpy.argmin¶

numpy.argmin(a, axis=None, out=None)[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.

Returns

index_arrayndarray of ints: Array of indices into the array. It has the same shape as a.shape with the dimension along axis removed.

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.min(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.max(x, axis=-1)
>>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1).squeeze(axis=-1)
array([2, 0])

numpy.nanargmax numpy.nanargmin