也许对ndarray有帮助:

def find_nearest(X, value):
    return X[np.unravel_index(np.argmin(np.abs(X - value)), X.shape)]

下面是@Ari Onasafari的scipy版本，回答“在向量数组中找到最近的向量”

In [1]: from scipy import spatial

In [2]: import numpy as np

In [3]: A = np.random.random((10,2))*100

In [4]: A
Out[4]:
array([[ 68.83402637,  38.07632221],
       [ 76.84704074,  24.9395109 ],
       [ 16.26715795,  98.52763827],
       [ 70.99411985,  67.31740151],
       [ 71.72452181,  24.13516764],
       [ 17.22707611,  20.65425362],
       [ 43.85122458,  21.50624882],
       [ 76.71987125,  44.95031274],
       [ 63.77341073,  78.87417774],
       [  8.45828909,  30.18426696]])

In [5]: pt = [6, 30]  # <-- the point to find

In [6]: A[spatial.KDTree(A).query(pt)[1]] # <-- the nearest point 
Out[6]: array([  8.45828909,  30.18426696])

#how it works!
In [7]: distance,index = spatial.KDTree(A).query(pt)

In [8]: distance # <-- The distances to the nearest neighbors
Out[8]: 2.4651855048258393

In [9]: index # <-- The locations of the neighbors
Out[9]: 9

#then 
In [10]: A[index]
Out[10]: array([  8.45828909,  30.18426696])

如果你的数组已经排序并且非常大，这是一个更快的解决方案:

def find_nearest(array,value):
    idx = np.searchsorted(array, value, side="left")
    if idx > 0 and (idx == len(array) or math.fabs(value - array[idx-1]) < math.fabs(value - array[idx])):
        return array[idx-1]
    else:
        return array[idx]

这可以扩展到非常大的阵列。如果不能假定数组已经排序，可以很容易地修改上面的内容以在方法中排序。对于小型数组来说，这是多余的，但一旦它们变大，这就快得多了。

我认为最python的方式是:

 num = 65 # Input number
 array = np.random.random((10))*100 # Given array 
 nearest_idx = np.where(abs(array-num)==abs(array-num).min())[0] # If you want the index of the element of array (array) nearest to the the given number (num)
 nearest_val = array[abs(array-num)==abs(array-num).min()] # If you directly want the element of array (array) nearest to the given number (num)

这是基本代码。你可以把它作为一个函数来使用

对于2d数组，要确定最近元素的i, j位置:

import numpy as np
def find_nearest(a, a0):
    idx = (np.abs(a - a0)).argmin()
    w = a.shape[1]
    i = idx // w
    j = idx - i * w
    return a[i,j], i, j

下面是一个处理非标量“values”数组的版本:

import numpy as np

def find_nearest(array, values):
    indices = np.abs(np.subtract.outer(array, values)).argmin(0)
    return array[indices]

如果输入是标量，则返回数字类型(例如int, float)的版本:

def find_nearest(array, values):
    values = np.atleast_1d(values)
    indices = np.abs(np.subtract.outer(array, values)).argmin(0)
    out = array[indices]
    return out if len(out) > 1 else out[0]