Question

I don’t understand why the following code behaves the way it does.

import numpy as np


nbr_arrays = 4
nbr_fields_per_array = 3
nbr_subfields_per_field = 2

# pre-allocate zeros list
zeros = np.zeros(nbr_subfields_per_field)

data = []
for array in range(nbr_arrays):

    # pre-allocate the subarray 
    empty_array = []
    for empty_array_index in range(nbr_fields_per_array):
        empty_array.append(zeros)

    # append pre subarray to data
    data.append(empty_array)

    # fill up data
    for j in range(nbr_fields_per_array):
        for k in range(nbr_subfields_per_field):
            data[array][j][k] = j*k*array

The generated output data reads now:

[[array([ 0.,  6.]), array([ 0.,  6.]), array([ 0.,  6.])],
 [array([ 0.,  6.]), array([ 0.,  6.]), array([ 0.,  6.])],
 [array([ 0.,  6.]), array([ 0.,  6.]), array([ 0.,  6.])],
 [array([ 0.,  6.]), array([ 0.,  6.]), array([ 0.,  6.])]]

Even zeros reads completely differently:

array([ 0.,  6.])

If I look at the identify of the different lists, this is what I get:

id(data[0][0])
Out[72]: 45790208

id(data[1][0])
Out[66]: 45790208

id(data[2][0])
Out[67]: 45790208

id(data[3][0])
Out[68]: 45790208

id(zeros)
Out[69]: 45790208

why are all the references the same? and why does zero suddenly contain non-zero values?

I’d really appreciate it if somebody could explain me what exactly is happening here, and how I have to modify my code to see the expected behaviour (output).

EDIT:

not using zeros but using [[0]*nbr_subfields_per_field for x in range(nbr_fields_per_array)] instead gives me the expected result. but why? why doesn’t the original code work?
Modified code that works:

data = []
for array in range(nbr_arrays):
    empty_array = [[0]*nbr_subfields_per_field for x in range(nbr_fields_per_array)]

    ''' this is causing the weird behaviour 
    empty_array = []
    for empty_array_index in range(nbr_fields_per_array):
        empty_array.append(zeros)
    '''

    data.append(empty_array)

    for j in range(nbr_fields_per_array):
        for k in range(nbr_subfields_per_field):
            data[array][j][k] = j*k*array

Answer 1

# pre-allocate zeros list
zeros = np.zeros(nbr_subfields_per_field)

This creates a single object.

for empty_array_index in range(nbr_fields_per_array):
    empty_array.append(zeros)

This keeps appending the same object.

Stop pre-allocating.