NumPy Array Copy vs View

 It is essential to comprehend the distinction between copying and viewing arrays in NumPy to ensure optimal memory management and prevent unforeseen behavior. Here is an analysis of the main ideas:

Copy:

1. Generates a fresh array with a distinct data buffer.
2. Changes made to the duplicate do not impact the initial array, and conversely.
3. Beneficial for data isolation, sending arrays to functions without unintended consequences, or updating data without changing the original. 

Typical techniques for duplicating:

• Create a copy of the array. Ex: arr.copy()
  
• Make a replica of the array 'arr'.  Ex: np.copy(arr)
  
• Shallow copy for one-dimensional arrays. Ex: arr[ : ]
  

View:

1. Offers an alternative viewpoint on the original data buffer in the array.
2. Modifications to the view are instantly mirrored in the original array, and conversely.
3. Beneficial for optimizing memory use when dealing with extensive arrays or when changes made to one array should be mirrored in another.

Typical techniques for generating perspectives:

• Using the slicing technique, for example, arr[1:3].
• reshape(), .transpose(), .ravel()
• Create a view using arr.view() explicitly.

Key Differences:

 

import numpy as np

arr = np.array([1, 2, 3])

# Copy
copied_arr = arr.copy()
copied_arr[0] = 10

print("Original array:", arr)  # Output: [1 2 3]
print("Copied array:", copied_arr)  # Output: [10 2 3]

# View
view_arr = arr.view()
view_arr[1] = 20

print("Original array:", arr)  # Output: [1 20 3]
print("View array:", view_arr)  # Output: [1 20 3]