我认为最好遍历字典，并“一次”在所有行和列中设置值：

>>> a = np.array([[1,2,3],[3,2,1]])

>>> a

array([[1, 2, 3],

[3, 2, 1]])

>>> d = {1 : 11, 2 : 22, 3 : 33}

>>> for k,v in d.iteritems():

... a[a == k] = v

...

>>> a

array([[11, 22, 33],

[33, 22, 11]])

编辑：

尽管它可能不如使用numpy.vectorize的DSM(非常好)的答案那么性感，但我对所有建议方法的测试表明，这种方法(使用@jamylak的建议)实际上要快一些：

from __future__ import division

import numpy as np

a = np.random.randint(1, 5, (500,500))

d = {1 : 11, 2 : 22, 3 : 33, 4 : 44}

def unique_translate(a,d):

u,inv = np.unique(a,return_inverse = True)

return np.array([d[x] for x in u])[inv].reshape(a.shape)

def vec_translate(a, d):

return np.vectorize(d.__getitem__)(a)

def loop_translate(a,d):

n = np.ndarray(a.shape)

for k in d:

n[a == k] = d[k]

return n

def orig_translate(a, d):

new_a = np.empty(a.shape)

for i,row in enumerate(a):

new_a[i,:] = map(d.get, row)

return new_a

if __name__ == '__main__':

import timeit

n_exec = 100

print 'orig'

print timeit.timeit("orig_translate(a,d)",

setup="from __main__ import np,a,d,orig_translate",

number = n_exec) / n_exec

print 'unique'

print timeit.timeit("unique_translate(a,d)",

setup="from __main__ import np,a,d,unique_translate",

number = n_exec) / n_exec

print 'vec'

print timeit.timeit("vec_translate(a,d)",

setup="from __main__ import np,a,d,vec_translate",

number = n_exec) / n_exec

print 'loop'

print timeit.timeit("loop_translate(a,d)",

setup="from __main__ import np,a,d,loop_translate",

number = n_exec) / n_exec

输出：

orig

0.222067718506

unique

0.0472617006302

vec

0.0357889199257

loop

0.0285375618935