Python中遍历列表结合str.contains统计扑克玩家操作次数问题

你遇到的问题根源是正则表达式里没有正确插入变量——你写的'(player).*(action)'是字面量字符串，Python不会把player和action变量的值替换进去，所以实际在匹配包含"player"和"action"这两个单词的日志，自然统计结果全是0。

第一步：修正单个操作的统计函数 #

我们需要用f-string拼接正则模式，同时用re.escape()处理玩家名里的特殊字符（比如Bob T.里的.，在正则里是通配符，需要转义），确保匹配准确：

import re
import pandas as pd

# 构造样本DataFrame（基于你提供的entry数据）
entry_data = [
    "-- ending hand #174 --",
    "\"Prof @ ZY_G_5ZOve\" gained 100",
    "\"tom_thumb @ g1PBaozt7k\" folds",
    "\"Prof @ ZY_G_5ZOve\" calls with 50",
    "\"tom_thumb @ g1PBaozt7k\" checks",
    "river: 9♦, 5♣, Q♥, 7♠ [K♠]",
    "\"Prof @ ZY_G_5ZOve\" checks",
    "\"tom_thumb @ g1PBaozt7k\" checks",
    "turn: 9♦, 5♣, Q♥ [7♠]",
    "\"Prof @ ZY_G_5ZOve\" checks",
    "\"tom_thumb @ g1PBaozt7k\" checks",
    "flop: [9♦, 5♣, Q♥]",
    "\"Prof @ ZY_G_5ZOve\" checks",
    "\"tom_thumb @ g1PBaozt7k\" calls with 50",
    "\"Bob T. @ fjZTXUGV2G\" folds",
    "\"danny G @ tNE1_lEFYv\" folds",
    "\"Prof @ ZY_G_5ZOve\" posts a big blind of 50",
    "\"tom_thumb @ g1PBaozt7k\" posts a small blind of 25",
    "-- starting hand #174 (Texas Hold'em) (dealer: \"Bob T. @ fjZTXUGV2G\") --",
    "-- ending hand #173 --",
    "\"tom_thumb @ g1PBaozt7k\" gained 475",
    "\"danny G @ tNE1_lEFYv\" folds",
    "\"Prof @ ZY_G_5ZOve\" folds",
    "\"tom_thumb @ g1PBaozt7k\" raises with 356",
    "flop: [4♥, A♠, 6♠]",
    "\"danny G @ tNE1_lEFYv\" calls with 150",
    "\"Prof @ ZY_G_5ZOve\" calls with 150",
    "\"tom_thumb @ g1PBaozt7k\" raises with 150",
    "\"Bob T. @ fjZTXUGV2G\" folds",
    "\"danny G @ tNE1_lEFYv\" calls with 50",
    "\"Prof @ ZY_G_5ZOve\" calls with 50",
    "\"tom_thumb @ g1PBaozt7k\" posts a big blind of 50",
    "\"Bob T. @ fjZTXUGV2G\" posts a small blind of 25",
    "-- starting hand #173 (Texas Hold'em) (dealer: \"danny G @ tNE1_lEFYv\") --"
]
df = pd.DataFrame({'entry': entry_data})

player_list = ['danny G', 'Jane', 'Prof', 'spn', 'tim', 'Bob T.', 'joon', 'tom_thumb']
action_list = ['call', 'fold']

def action_amount(df, player_list, action):
    for player in player_list:
        # 转义玩家名特殊字符，构造精准正则模式
        pattern = fr'"{re.escape(player)} @ .*{action}'
        # 统计匹配的日志行数
        action_number = len(df[df['entry'].str.contains(pattern, regex=True)])
        print(f'{player} {action}ed {action_number} times.')

# 测试单个操作统计
action_amount(df, player_list, 'call')

运行这段代码，就能得到你预期的输出：

danny G called 2 times.
Jane called 0 times.
Prof called 3 times.
spn called 0 times.
tim called 0 times.
Bob T. called 0 times.
joon called 0 times.
tom_thumb called 1 times.

第二步：优化为同时遍历玩家和操作列表 #

如果要一次性统计所有玩家的所有操作，可以修改函数，生成更结构化的统计结果（比如返回DataFrame）：

def all_action_stats(df, player_list, action_list):
    # 初始化统计字典
    stats = {player: {action:0 for action in action_list} for player in player_list}
    
    for player in player_list:
        player_escaped = re.escape(player)
        for action in action_list:
            pattern = fr'"{player_escaped} @ .*{action}'
            stats[player][action] = len(df[df['entry'].str.contains(pattern, regex=True)])
    
    # 转换为DataFrame方便查看和后续处理
    stats_df = pd.DataFrame.from_dict(stats, orient='index')
    return stats_df

# 生成全量统计结果
result_df = all_action_stats(df, player_list, action_list)
print(result_df)

输出结果：

call  fold
danny G        2     2
Jane           0     0
Prof           3     1
spn            0     0
tim            0     0
Bob T.         0     2
joon           0     0
tom_thumb      1     1

关键说明 #

1. 正则转义：用re.escape()处理玩家名，避免像Bob T.里的.被正则当成通配符，导致匹配错误。
2. f-string拼接：用fr""构造原始字符串（避免转义字符冲突），把变量直接插入正则模式中。
3. 匹配逻辑：模式"{player} @ .*{action}"完全对应日志格式，确保只统计目标玩家的指定操作。

内容的提问来源于stack exchange，提问作者pnat_1040