numpy：根据索引数组组合多个数组

Question

I have 2 arrays of different sizes m and n , for instance:

x = np.asarray([100, 200])
y = np.asarray([300, 400, 500])

I also have an integer array of size m+n , for instance:

indices = np.asarray([1, 1, 0, 1 , 0])

I'd like to combine x and y into an array z of size m+n , in this case:

expected_z = np.asarray([300, 400, 100, 500, 200])

In details:

• The 1st value of indices is 1, so the 1st value of z should come from y . Therefore 300 .
• The 2nd value of indices is 1, so the 2nd value of z should also come from y . Therefore 400
• The 3rd value of indices is 0, so the 3rd value of z should this time come from x . Therefore 100
• ...

How could I do that efficiently in NumPy?

Thanks in advance!

Answer 1

Make an output array and use boolean indexing to assign x and y into the correct slots of the output:

z = numpy.empty(len(x)+len(y), dtype=x.dtype)
z[indices==0] = x
z[indices==1] = y

Answer 2

out will be your desired output:

out = indices.copy()
out[np.where(indices==0)[0]] = x
out[np.where(indices==1)[0]] = y

or as the above answer suggested, simply do:

out = indices.copy()
out[indices==0] = x
out[indices==1] = y

Answer 3

i hope this could help you:

x          = np.asarray([100, 200])
y          = np.asarray([300, 400, 500])
indices    = np.asarray([1, 1, 0, 1 , 0])
expected_z = np.asarray([])
x_indice   = 0
y_indice   = 0

for i in range(0,len(indices)):
    if indices[i] == 0:
        expected_z = np.insert(expected_z,i,x[x_indice])
        x_indice += 1
    else:
        expected_z = np.insert(expected_z,i,y[y_indice])
        y_indice += 1

expected_z

and the output is:

output : array([300., 400., 100., 500., 200.])

PS always make sure that len(indices) == len(x) + len(y) and :

• the values that are coming from y == len(y)
• the values that are coming from x == len(x)