Speed of numpy.roll vs list with append and pop vs custom numpy rolling using slices

custom_roll.py

def my_roll(arr, x):
    arr[0:-1] = arr[1:]
    arr[-1] = x
    return arr

using_custom_roll.py

%%timeit
x_list = range(100000)
x_arr = np.array(x_list)

for i in range(1000):
    x_arr = my_roll(x_arr, 101)
    
# On my machine: 10 loops, best of 3: 52.3 ms per loop

using_list.py

%%timeit
x_list = range(100000)
x_arr = np.array(x_list)

for i in range(1000):
    x_list.append(1)
    x_list.pop(0)
    
# On my machine: 10 loops, best of 3: 53.5 ms per loop

using_numpy_roll.py

%%timeit
x_list = range(100000)
x_arr = np.array(x_list)

for i in range(1000):
    x_arr = np.roll(x_arr, -1)
    x_arr[-1] = 1
    
# On my machine: 1 loop, best of 3: 288 ms per loop