如何基于条件映射字典为pandas DataFrame新增匹配结果列

首先调整原有映射字典的结构为按匹配优先级排序的规则列表，再通过自定义函数逐行匹配即可得到目标结果，完整代码如下：

import pandas as pd
import numpy as np

# 原始映射字典
dict_map = {
    "Yes" : {'Sex':'F', 'Pregnant': 'Y'},
    "No" : {'Sex':'F', 'Pregnant': 'N'},
    "N/A" : {'Sex': 'M'},
}

# 原始DataFrame
df = pd.DataFrame(
{
    'ID': ['AB01', 'AB02', 'AB03', 'AB04', 'AB05','AB06'],
    'Sex': ["F","M","M",np.nan,"F","F"],
    'Pregnant': ['Y', 'N',np.nan, 'Y', '', 'N']
 }
 )
cols_of_interest = ["Sex","Pregnant"]

# 调整为按优先级排序的匹配规则（条件越多优先级越高）
match_rules = [(res, cond) for res, cond in dict_map.items()]
# 如需自定义匹配顺序可手动定义规则：
# match_rules = [("Yes", {'Sex':'F', 'Pregnant': 'Y'}), ("No", {'Sex':'F', 'Pregnant': 'N'}), ("N/A", {'Sex': 'M'})]

def match_row(row):
    for result, conditions in match_rules:
        is_match = True
        for col, target in conditions.items():
            row_val = row[col]
            # 过滤空值、空字符串等无效值
            if pd.isna(row_val) or str(row_val).strip() == "":
                is_match = False
                break
            if row_val != target:
                is_match = False
                break
        if is_match:
            return result
    return np.nan

# 生成结果列
df["Result"] = df.apply(match_row, axis=1)

# 查看最终输出
print(df)

运行后生成的Result列即为需求的计算结果。

内容的提问来源于stack exchange，提问作者Fazli