`numpy:argmin`

source: /numpy/_core/fromnumeric.py :1321

Signature

   def     argmin (    a    ,    axis    =  None   ,    out    =  None   ,  * ,     keepdims    =  <no value>     )

Summary

Returns the indices of the minimum values along an axis.

Parameters

a : array_like: Input array.
axis : int, optional: By default, the index is into the flattened array, otherwise along the specified axis.
out : array, optional: If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype.
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 array.
versionadded 1.22.0

Returns

index_array : ndarray of ints: Array of indices into the array. It has the same shape as a.shape with the dimension along axis removed. If keepdims is set to True, then the size of axis will be 1 with the resulting array having same shape as a.shape.

Notes

In case of multiple occurrences of the minimum values, the indices corresponding to the first occurrence are returned.

Examples

import numpy as np
a = np.arange(6).reshape(2,3) + 10
a

✓

np.argmin(a)

✗

np.argmin(a, axis=0)
np.argmin(a, axis=1)

✓

Indices of the minimum elements of a N-dimensional array:

a.flat[np.argmin(a)]

✗

ind = np.unravel_index(np.argmin(a, axis=None), a.shape)

✓

ind
a[ind]

✗

b = np.arange(6) + 10
b[4] = 10
b

✓

np.argmin(b)  # Only the first occurrence is returned.

✗

x = np.array([[4,2,3], [1,0,3]])
index_array = np.argmin(x, axis=-1)
np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1)
np.take_along_axis(x, np.expand_dims(index_array, axis=-1),
    axis=-1).squeeze(axis=-1)

✓

Setting `keepdims` to `True`,

x = np.arange(24).reshape((2, 3, 4))
res = np.argmin(x, axis=1, keepdims=True)
res.shape

✓

Aliases

numpy.argmin