Working with pandas DataFrames (Numpy arrays) of strings in Cython

How can one read/write pandas DataFrames (Numpy arrays) of strings in Cython?

It works just fine when I work with integers or floats:

# Cython file numpy_.pyx
@boundscheck(False)
@wraparound(False)
cpdef fill(np.int64_t[:,::1] arr):
    arr[0,0] = 10
    arr[0,1] = 11
    arr[1,0] = 13
    arr[1,1] = 14

# Python code
import numpy as np
from numpy_ import fill
a = np.array([[0,1,2],[3,4,5]], dtype=np.int64)
print(a)
fill(a)
print(a)

gives

>>> a = np.array([[0,1,2],[3,4,5]], dtype=np.int64)
>>> print(a)
[[0 1 2]
 [3 4 5]]
>>> fill(a)
>>> print(a)
[[10 11  2]
 [13 14  5]]

Also, the following code

# Python code
import numpy as np, pandas as pd
from numpy_ import fill
a = np.array([[0,1,2],[3,4,5]], dtype=np.int64)
df = pd.DataFrame(a)
print(df)
fill(df.values)
print(df)

gives

>>> a = np.array([[0,1,2],[3,4,5]], dtype=np.int64)
>>> df = pd.DataFrame(a)
>>> print(df)
   0  1  2
0  0  1  2
1  3  4  5
>>> fill(df.values)
>>> print(df)
    0   1  2
0  10  11  2
1  13  14  5

However, I am having hard time figuring out how to do the same thing when the input is an array of strings. For example, how can I read of modify a Numpy array or a pandas DataFrame:

a2 = np.array([['000','111','222'],['333','444','555']], dtype='U3')
df2 = pd.DataFrame(a2)

and, let us say, the goal is to change through Cython

'000' -> 'AAA'; '111' -> 'BBB'; '222' -> 'CCC'; '333' -> 'DDD'

I did read the following NumPy documentation page and the following Cython documentation page, but still can not figure out what to do.

Thank you very much for your help!