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 neveropen = gfg.__array__(float) 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([[1.1, 2, 3.3, 4, 5], [6, 5.2, 4, 3, 2.2]]) # applying ndarray.__array__() method neveropen = gfg.__array__(int) print(neveropen) |
Output:
[[1 2 3 4 5] [6 5 4 3 2]]
