Numpy concatenate is slow: any alternative approach?

Question

I am running the following code:

for i in range(1000)
    My_Array=numpy.concatenate((My_Array,New_Rows[i]), axis=0)

The above code is slow

Answer 1

Assume you have a large list of 2D numpy arrays, with the same number of columns and different number of rows like this :

x = [numpy_array1(r_1, c),......,numpy_arrayN(r_n, c)]

concatenate like this:

while len(x) != 1:
    if len(x) == 2:
        x = np.concatenate((x[0], x[1]))
        break
    for i in range(0, len(x), 2):
        if (i+1) == len(x):
            x[0] = np.concatenate((x[0], x[i]))
        else:
            x[i] = np.concatenate((x[i], x[i+1]))

    x = x[::2]