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 geeks = gfg.__copy__()
print (geeks)
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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 geeks = gfg.__copy__()
# Change the data element geeks[ 0 ][ 2 ] = 10
print (gfg, end = '\n\n' )
print (geeks)
|
Output:
[[1 2 3 4 5] [6 5 4 3 2]] [[ 1 2 10 4 5] [ 6 5 4 3 2]]
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