Introduction to Python NumPy Array Copy vs View

Python NumPy Array Copy vs View

When working with arrays, it's important to understand the concepts of array copy and view.

Both copy and view provide different ways to access and manipulate array data.

Let's explore the difference between them:

Array Copy

• A copy of an array is a separate array object with its own memory.
• Modifying the copy does not affect the original array, and vice versa.
• The copy() function is used to create a copy of an array explicitly.

As an example:

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
copy_arr = arr.copy()  # Create a copy of the array
copy_arr[0] = 10  # Modify the copy

print(arr)       # Output: [1 2 3 4 5]
print(copy_arr)  # Output: [10 2 3 4 5]

In the example above:

• Modifying the copy_arr does not affect the original arr.

Array View

• A view of an array is a different way to look at the same data.
• The view refers to the same memory location as the original array, but it has a different shape or a different way to access the data.
• Modifying the view will affect the original array, and vice versa.
• Views can be created by array slicing or using the view() method.

As an example:

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
view_arr = arr[2:]  # Create a view of the array

view_arr[0] = 10  # Modify the view

print(arr)      # Output: [1 2 10 4 5]
print(view_arr) # Output: [10 4 5]

In the example above:

• Modifying the view_arr also modifies the original arr.