Learn Copy vs View

NumPy, understanding the concepts of copy and view is important for managing memory efficiently and avoiding unintended changes to arrays. Let's explore the differences between copy and view:

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Copy

• When you create a copy of an array, you get a new array with its own data.
• Modifying the copy will not affect the original array, and vice versa.
• Copying an array creates a new memory allocation for the data.
• You can create a copy explicitly using the copy() method.

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = arr1.copy()
arr2[0] = 100
print(arr1) # Output: [1 2 3]
print(arr2) # Output: [100 2 3]

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View

• A view is a different way of looking at the same data. It's a new array object that refers to the same underlying data as the original array.
• Modifying the view will affect the original array, and vice versa.
• Creating a view does not involve copying the data; it shares the same memory allocation.
• Views can be created using slicing or using the view() method.

import numpy as np
arr1 = np.array([1, 2, 3, 4, 5])
view_arr = arr1[1:4]
view_arr[0] = 100
print(arr1) # Output: [ 1 100 3 4 5]
print(view_arr) # Output: [100 3 4]

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In summary:
Copy creates a new array with its own data, modifying the copy doesn't affect the original array, and vice versa.
View creates a new array object that shares the same data as the original array, modifying the view affects the original array, and vice versa.

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