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

Notes

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

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

Returns

index_array : ndarray of ints

Array of indices into the array. It has the same shape as :None:None:`a.shape` with the dimension along :None:None:`axis` removed. If :None:None:`keepdims` is set to True, then the size of :None:None:`axis` will be 1 with the resulting array having same shape as :None:None:`a.shape`.

Returns the indices of the minimum values along an axis.

See Also

amin

The minimum value along a given axis.

argmax

ndarray.argmin

take_along_axis

Apply np.expand_dims(index_array, axis) from argmin to an array as if by calling min.

unravel_index

Convert a flat index into an index tuple.

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 :None:None:`keepdims` to :None:None:`True`,

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

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.series.Series.idxmin numpy.amin numpy.nanargmin numpy.matrixlib.defmatrix.matrix.argmin numpy.argmax dask.array.core.Array.argmin

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