When running the following script, I’m getting significant performance differences between plain python and numba, that I have difficulty explaining/understanding. Anybody any idea why numba is 100 times slower than python here?

```
import numba
import numpy as np
from numba.typed import List
from numba import types
@numba.njit
def test_nb(names, search):
idx = {
nm: i
for i, nm in enumerate(names)
}
cnt = 0
for s in search:
if s in idx:
cnt += 1
return cnt
def test_py(names, search):
idx = {
nm: i
for i, nm in enumerate(names)
}
cnt = 0
for s in search:
if s in idx:
cnt += 1
return cnt
dt = np.dtype('U128')
search_np = np.array([f"{i}" for i in range(0, 2000, 100)], dtype=dt)
search = [f"{i}" for i in range(0, 2000, 100)]
lst = List(lsttype=types.ListType(numba.from_dtype(dt)))
names = [f"{i}" for i in range(1000)]
for nm in names:
lst.append(nm)
# warmup
test_nb(lst, search_np)
import time
t1 = time.time()
test_nb(lst, search_np)
t2 = time.time()
print(f"exectime numba: {(t2 - t1) * 1000}ms")
t1 = time.time()
test_py(names, search_np)
t2 = time.time()
print(f"exectime python: {(t2 - t1) * 1000}ms")
print(f"numba version = {numba.__version__}")
```

On my system this gives:

```
exectime numba: 45.153141021728516ms
exectime python: 0.24938583374023438ms
numba version = 0.56.3
```