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I am working with (lists of) lists of numpy arrays. As a bare bones example, consider this piece of code:

a = [np.zeros(5)]
b = a.copy()
b[0] += 1

Here, I copy a list of one array from a to b. However, the array itself is not copied, so:

print(a)
print(b)

both give [array([1., 1., 1., 1., 1.])]. If I want to make a copy of the array as well, I could do something like:

b = [arr.copy() for arr in a]

and a would remain unchanged. This works well for a simple list, but it becomes more complicated when working with nested lists of arrays where the number of arrays in each list is not always the same.

Is there a simple way to copy a multi-level list and every object that it contains without keeping references to the objects in the original list? Basically, I would like to avoid nested loops as well as dealing with the size of each individual sub-list.

JD80121
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2 Answers2

3

What you are looking for is a deepcopy

import numpy as np
import copy
a = [np.zeros(5)]
b = copy.deepcopy(a)
b[0] += 1  # a[0] is not changed

This is actually method recommended in numpy doc for the deepcopy of object arrays.

Mederic Fourmy
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  • Exactly what I needed! I guess this solution would work as well with, say, a custom class that contains other objects such as arrays? – JD80121 Dec 11 '21 at 20:00
  • Yes it should indeed, more about alternatives to deepcopy for custom objects here: https://stackoverflow.com/questions/39028978/copying-nested-custom-objects-alternatives-to-deepcopy – Mederic Fourmy Dec 26 '21 at 14:53
1

You need to use deepcopy.

import numpy as np
import copy

a = [np.zeros(5)]
b = copy.deepcopy(a)
b[0] += 1

print(a)
print(b)

Result:

[array([0., 0., 0., 0., 0.])]
[array([1., 1., 1., 1., 1.])]
Fauzaan
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