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Python NumPy full_like() Function Example


Python NumPy full_like() is an inbuilt function that returns the new array with the same shape and type as a given array. The full_like() function returns a full array with the same shape and type as a given array. The full_like() numpy function contains four parameters and is used to return an array of the similar shape and size as of the given array. 

Python NumPy full_like()

Python full_like() is defined under numpy, which can be imported as import numpy as np, and we can create multidimensional arrays and derive other mathematical statistics with the help of numpy, which is a library in Python. The full_like() function returns the new array of given shape and type, filled with fill_value.


numpy.full_like(shape, order, dtype, subok ) 


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, which represents its order in the memory. (C_contiguous or F_contiguous). The third parameter is the data type of the returned array. It is optional and has float value by default. The fourth parameter is a bool parameter, 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 full_like() function in Python.


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))


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 full_like() function also pass -3 as the input for the new array.


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))



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().

See also

Python NumPy bmat()

Python NumPy asmatrix()

Python NumPy diag_indices()

Python NumPy diag()

Python NumPy diagflat()

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