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NumPy - Copies & Views
NumPy - Copies & Views
While executing the functions, some of them return a copy of the input array, while some return the view. When the contents are physically stored in another location, it is called Copy. If on the other hand, a different view of the same memory content is provided, we call it as View.
No Copy
Simple assignments do not make the copy of array object. Instead, it uses the same id() of the original array to access it. The id() returns a universal identifier of Python object, similar to the pointer in C.
Furthermore, any changes in either gets reflected in the other. For example, the changing shape of one will change the shape of the other too.
Example
import numpy as np a = np.arange(6) print Our array is: print a print Applying id() function: print id(a) print a is assigned to b: b = a print b print b has same id(): print id(b) print Change shape of b: b.shape = 3,2 print b print Shape of a also gets changed: print a
It will produce the following output −
Our array is: [0 1 2 3 4 5] Applying id() function: 139747815479536 a is assigned to b: [0 1 2 3 4 5] b has same id(): 139747815479536 Change shape of b: [[0 1] [2 3] [4 5]] Shape of a also gets changed: [[0 1] [2 3] [4 5]]
View or Shallow Copy
NumPy has ndarray.view() method which is a new array object that looks at the same data of the original array. Unpke the earper case, change in dimensions of the new array doesn’t change dimensions of the original.
Example
import numpy as np # To begin with, a is 3X2 array a = np.arange(6).reshape(3,2) print Array a: print a print Create view of a: b = a.view() print b print id() for both the arrays are different: print id() of a: print id(a) print id() of b: print id(b) # Change the shape of b. It does not change the shape of a b.shape = 2,3 print Shape of b: print b print Shape of a: print a
It will produce the following output −
Array a: [[0 1] [2 3] [4 5]] Create view of a: [[0 1] [2 3] [4 5]] id() for both the arrays are different: id() of a: 140424307227264 id() of b: 140424151696288 Shape of b: [[0 1 2] [3 4 5]] Shape of a: [[0 1] [2 3] [4 5]]
Spce of an array creates a view.
Example
import numpy as np a = np.array([[10,10], [2,3], [4,5]]) print Our array is: print a print Create a spce: s = a[:, :2] print s
It will produce the following output −
Our array is: [[10 10] [ 2 3] [ 4 5]] Create a spce: [[10 10] [ 2 3] [ 4 5]]
Deep Copy
The ndarray.copy() function creates a deep copy. It is a complete copy of the array and its data, and doesn’t share with the original array.
Example
import numpy as np a = np.array([[10,10], [2,3], [4,5]]) print Array a is: print a print Create a deep copy of a: b = a.copy() print Array b is: print b #b does not share any memory of a print Can we write b is a print b is a print Change the contents of b: b[0,0] = 100 print Modified array b: print b print a remains unchanged: print a
It will produce the following output −
Array a is: [[10 10] [ 2 3] [ 4 5]] Create a deep copy of a: Array b is: [[10 10] [ 2 3] [ 4 5]] Can we write b is a False Change the contents of b: Modified array b: [[100 10] [ 2 3] [ 4 5]] a remains unchanged: [[10 10] [ 2 3] [ 4 5]]Advertisements