Need help in understanding the loop speed with timeit function in python

I need help in understanding the %timeit function works in the two programs.

Program A

a = [1,3,2,4,1,4,2]
%timeit [val + 5 for val in a]
830 ns ± 45.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

Program B

import numpy as np
a = np.array([1,3,2,4,1,4,2])
%timeit [a+5]
1.07 µs ± 23.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

My confusion:

1. µs is bigger than ns. How does the NumPy function execute slower than for loop here?
2. 1.07 µs ± 23.7 ns per loop... why is the loop speed calculated in ns and not in µs?

Numpy adds an overhead, this will impact the speed on small datasets. Vectorization is mostly useful when using large datasets.

You must try on larger numbers:

N = 10_000_000


a = list(range(N))
%timeit [val + 5 for val in a]

import numpy as np
a = np.arange(N)
%timeit a+5

Output:

1.51 s ± 318 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

55.8 ms ± 3.63 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)