Deep Copies

 

Though views are separate array objects, they save memory by sharing element data from other arrays. However, when sharing mutable values, sometimes it’s necessary to create a deep copy with independent copies of the original data. This is especially important in multi-core programming, where separate parts of your program could attempt to modify your data at the same time, possibly corrupting it.

The array method copy returns a new array object with a deep copy of the original array object’s data. First, let’s create an array and a deep copy of that array:

In [1]: import numpy as np
In [2]: numbers = np.arange(1, 6)
In [3]: numbers

Out[3]: array([1, 2, 3, 4, 5])

In [4]: numbers2 = numbers.copy()
In [5]: numbers2

Out[5]: array([1, 2, 3, 4, 5])

To prove that numbers2 has a separate copy of the data in numbers, let’s modify an element in numbers, then display both arrays:

In [6]: numbers[1] *= 10
In [7]: numbers

Out[7]: array([ 1, 20, 3, 4, 5])
In [8]: numbers2

Out[8]: array([ 1, 2, 3, 4, 5])

As you can see, the change appears only in numbers.