Python Implementation of a Simplified ZhaJinHua Card Game with Scoring and Statistics

The article introduces the classic Chinese card game ZhaJinHua (also known as "Three‑Card Poker") and demonstrates how to implement a simplified version using Python.

Game Rules : A standard 52‑card deck (without jokers) is used; each player receives three cards. Hand types are ranked from highest to lowest as follows: Straight Flush, Three of a Kind ("豹子"), Straight, Flush, Pair, and High Card. The article notes that the implemented rules differ slightly from the traditional game.

1. Deck Preparation

suit = ["黑桃", "红心", "方块", "梅花"]
num = [str(i) for i in range(2, 11)] + ["J", "Q", "K", "A"]
score_map = {}
for s in suit:
    count = 2
    for n in num:
        score_map[f"{s}{n}"] = count
        count += 1

2. Player Enrollment

players = [f"p{i}" for i in range(1, 6)]

3. Dealing Cards

def get_pk_lst(pls, pks):
    result = []
    for p in pls:
        pk = sample(pks, 3)
        for _pk in pk:
            pks.remove(_pk)
        result.append({"name": p, "poker": pk})
    return result

pokers = list(score_map.keys())
poker_grp = get_pk_lst(players, pokers)

4. Hand Evaluation and Scoring

def calculate(_score_map, pk_lst):
    n_lst = list(map(lambda x: _score_map[x], pk_lst))
    same_suit = len(set([pk[:2] for pk in pk_lst])) == 1
    continuity = sorted(n_lst) == [i for i in range(min(n_lst), max(n_lst)+1)] or set(n_lst) == {14, 2, 3}
    check = len(set(n_lst))
    if not same_suit and not continuity and check == 3:
        return sum(n_lst), "单张"
    if not same_suit and check == 2:
        w = [i for i in n_lst if n_lst.count(i) == 2][0]
        single = [i for i in n_lst if i != w][0]
        return w*2*2 + single, "对子"
    if same_suit and not continuity:
        return sum(n_lst)*9, "金花"
    if continuity and not same_suit:
        return sum(n_lst)*81, "顺子"
    if check == 1:
        return sum(n_lst)*666, "豹子"
    if continuity and same_suit:
        return sum(n_lst)*999, "同花顺"

5. Determining the Winner

def compare(_score_map, pk_grp):
    for p in pk_grp:
        p["score"], p["type"] = calculate(_score_map, p["poker"])
    print("开牌结果------")
    for p in pk_grp:
        print(p)
    print("赢家是------")
    best = max(pk_grp, key=lambda x: x["score"])["name"]
    print(best)
    return pk_grp

6. Running a Single Game

def show(_score_map, _players):
    pokers = list(_score_map.keys())
    poker_grp = get_pk_lst(_players, pokers)
    return compare(_score_map, poker_grp)

7. Batch Simulation and Statistics

def start_game(_score_map, _players, freq=1):
    type_lst = []
    for _ in range(freq):
        grp = show(_score_map, _players)
        type_lst += [t["type"] for t in grp]
    c = Counter(type_lst)
    total = sum(c.values())
    for item, cnt in c.items():
        print(f"{item}频率：{cnt/total:.2%}")

8. Main Execution

if __name__ == "__main__":
    # deck preparation (as above)
    players = [f"p{i}" for i in range(1, 6)]
    start_game(score_map, players, freq=100000)

The script runs 100,000 simulated games, prints the frequency of each hand type, and demonstrates that the empirical probabilities closely match theoretical values (e.g., high‑card ~74.37%, pair ~16.95%, flush ~4.97%, straight ~3.25%, three‑of‑a‑kind ~0.24%, straight‑flush ~0.22%).