NumPyArray Copy vs View
The Difference Between Copy and View
The main difference between a copy and a view of an array is thatthe copy is a new array, and the view is just a view of the original array.
The copyowns the data and any changes made to the copy will not affect original array, and any changes made to the original array will not affect the copy.
The viewdoes not own the data and any changes made to the view will affect the original array, and any changes made to the original array will affect the view.
COPY:
Example
Make a copy, change the original array, and display both arrays:
arr = np.array([1, 2, 3, 4, 5])
x = arr.copy()
arr[0] = 42
print(arr)
print(x)
The copy SHOULD NOT be affected by the changes made to the original array.
VIEW:
Example
Make a view, change the original array, and display both arrays:
arr = np.array([1, 2, 3, 4, 5])
x = arr.view()
arr[0] = 42
print(arr)
print(x)
The view SHOULD be affected by the changes made to the original array.
Make Changes in the VIEW:
Example
Make a view, change the view, and display both arrays:
arr = np.array([1, 2, 3, 4, 5])
x = arr.view()
x[0] = 31
print(arr)
print(x)
The original array SHOULD be affected by the changes made to the view.
Check if Array Owns its Data
As mentioned above, copiesowns the data, and viewsdoes not own the data, but how can we check this?
Every NumPy array has the attributebase that returnsNone if the array owns the data.
Otherwise, thebase attribute refers to the original object.
Example
Print the value of the base attribute to check if an array owns it's data or not:
arr = np.array([1, 2, 3, 4, 5])
x = arr.copy()
y = arr.view()
print(x.base)
print(y.base)
The copy returnsNone.
The view returns the original array.
