The np.full_like() is defined under the numpy library, which can be imported as import numpy as np, and we can create multidimensional arrays.
Numpy full_like()
The np.full_like() is a numpy library function that returns the new array with the same shape and type as a given array. The full_like() function contains four parameters and returns an array of the similar shape and size as the given array.
Syntax
numpy.full_like(shape, order, dtype, subok )
Parameters
It takes 4 parameters, out of which 2 parameters are optional.
The first parameter is the shape, which represents the number of rows.
The second parameter is the order, representing its order in the memory. (C_contiguous or F_contiguous).
The third parameter is the dtype(datatype) of the returned array. It is optional and has a float value by default.
The fourth parameter is a bool parameter subok, which checks if we have to create a sub-class of the main array or not.
Return Value
It returns a ndarray of the same shape and size.
Programs on full_like() method in Python
Write a program to show the working of the full_like() function in Python.
# app.py
import numpy as np
arr = np.arange(15, dtype=int).reshape(3, 5)
print("arr before full_like : \n", arr)
print("\narr after full_like : \n", np.full_like(arr, 15.0))
Output
python3 app.py arr before full_like : [[ 0 1 2 3 4] [ 5 6 7 8 9] [10 11 12 13 14]] arr after full_like : [[15 15 15 15 15] [15 15 15 15 15] [15 15 15 15 15]]
In this example, we can see that using full_like(), we have preserved arrays’ shape and size and returned a new array with the new value.
Write a program to take a 4×4 matrix and then apply the full_like() function that also passes -3 as the input for the new array.
# app.py
import numpy as np
arr2 = np.arange(16, dtype=float).reshape(4, 4)
print("\n\narr2 before full_like : \n", arr2)
# using full_like
print("\narr2 after full_like : \n", np.full_like(arr2, -3))
Output
python3 app.py arr2 before full_like : [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.] [12. 13. 14. 15.]] arr2 after full_like : [[-3. -3. -3. -3.] [-3. -3. -3. -3.] [-3. -3. -3. -3.] [-3. -3. -3. -3.]]
In this example, we can see that we have passed a 4×4 array, and the value in the new array is negative, which is -3 hence showing the attributes of full_like().
