With the help of Numpy ndarray.__copy__() method, we can make a copy of all the data elements that is present in numpy array. If you change any data element in the copy, it will not affect the original numpy array.
Syntax :
numpy.__copy__()Return : Copy of all the data elements
Example #1 :
In this example we can see that with the help of numpy.__copy__() method we are making the copy of an elements.
# import the important module in python import numpy as np # make an array with numpy gfg = np.array([1, 2, 3, 4, 5]) # applying ndarray.__copy__() method neveropen = gfg.__copy__() print(neveropen) |
Output:
[1 2 3 4 5]
Example #2 :
# import the important module in python import numpy as np # make an array with numpy gfg = np.array([[1, 2, 3, 4, 5], [6, 5, 4, 3, 2]]) # applying ndarray.__copy__() method neveropen = gfg.__copy__() # Change the data element neveropen[0][2] = 10 print(gfg, end ='\n\n') print(neveropen) |
Output:
[[1 2 3 4 5] [6 5 4 3 2]] [[ 1 2 10 4 5] [ 6 5 4 3 2]]