With the help of ndarray.__array__()
method, we can create a new array as we want by giving a parameter as dtype and we can get a copy of an array that doesn’t change the data element of original array if we change any element in the new one.
Syntax : ndarray.__array__()
Return :
- Returns either a new reference to self if dtype is not given
- New array of provided data type if dtype is different from the current dtype of the array.
Example #1 :
In this example we can see that we change the dtype of a new array by just using ndarray.__array__()
method.
# 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.__array__() method geeks = gfg.__array__( float )
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([[ 1.1 , 2 , 3.3 , 4 , 5 ],
[ 6 , 5.2 , 4 , 3 , 2.2 ]])
# applying ndarray.__array__() method geeks = gfg.__array__( int )
print (geeks)
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Output:
[[1 2 3 4 5] [6 5 4 3 2]]