numba - TypingError: cannot determine Numba type of <class 'builtin_function_or_method'>

Question

I have a simple function to rank poker hands (the hands are strings).

I call it with rA,rB = rank(a),rank(b) and here is my implementation. Works well without @jit(nopython=True), but with it, it fails:

   File "...poker.py", line 190, in <module>
        rA,rB = rank(a),rank(b)
    
      File "C:\Continuum\anaconda3\lib\site-packages\numba\dispatcher.py", line 344, in _compile_for_args
        reraise(type(e), e, None)
    
      File "C:\Continuum\anaconda3\lib\site-packages\numba\six.py", line 658, in reraise
        raise value.with_traceback(tb)

TypingError: cannot determine Numba type of <class 'builtin_function_or_method'>

---

from numba import jit
from numba.types import string

@jit(nopython=True)
def rank(hand):
#    assert(len(hand) == 5)
    rank = "N/A"
    
    p = pd.Series([h[0] for h in hand]).value_counts()
    v = sorted(set(pd.Series([h[0] for h in hand]).values), reverse=True)
    s = sorted(hand, key=lambda k:k[0])    
    z = zip(s,s[1:])
    
    if all(x[0]==y[0]-1 for x,y in z):
        rank = "Straight "
    
    if len(set([h[1] for h in hand])) == 1:
        rank += "Flush "
    
    if "Straight Flush" in rank and sum([h[0] for h in hand]) == sum([10,11,12,13,14]):
        rank = "Royal Flush"
    
    elif p[p.idxmax()] == 4:
        rank = "4 Of A Kind : %d" % p.idxmax()
        
    elif p[p.idxmax()] == 3 and p[p.idxmin()] == 1:
        rank = "3 Of A Kind : %d" % p.idxmax()
        
    elif p[p.idxmax()] == 3 and p[p.idxmin()] == 2:
        rank = "Full House : %d,%d" % (p.idxmax(), p.idxmin())
        
    elif p[p.idxmax()] == 2:
        max2 = p.nlargest(2)
        
        if list(max2) == [2,2]:
            max2 = sorted(list(max2.keys()), reverse=True)
            rank = "2 Pairs : %d,%d" % (max2[0],max2[1])
        else:
            rank = "Pair : %d" % p.idxmax()
    
    else:
        rank = "High Card : %d" % v[0]
    

    return rank

Answer 1

Pandas and several other function calls in your code will not work with nopython=True. The available libraries that can be used with numba jit in nopython is fairly limited (pretty much only to numpy arrays and certain python builtin libraries). You can find more information here

Answer 2

Per the deprecation recommendations, it's very reasonable that code which doesn't compile with @jit(nopython=True) could be faster without the decorator.

Anecdotally, I found the trivial example I landed here researching was notably faster when simply passing the Pandas columns directly to my function to vectorize the operation, rather than using numba and paying the method overhead of the columns for a numpy array.

However, it continues with the expected argument to clear this warning

If there is benefit to having the @jit decorator present, then to be future proof supply the keyword argument forceobj=True to ensure the function is always compiled in object mode.