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.
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.
# 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))
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 full_like() function also pass -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))
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().