基于指定条件批量更新DataFrame中唯一Id对应所有行的score值

问题示例 #

原始DataFrame #

Id           condition1        condition2       score
A               attempt           pass            0
A               attempt           fail            0
B               attempt           pass            0
B               attempt           level_1         0
B               attempt           fail            0
C               attempt           fail            0
D               attempt           fail            0

目标DataFrame #

Id           condition1        condition2       score
A               attempt           pass            1
A               attempt           fail            1
B               attempt           pass            1
B               attempt           level_1         1
B               attempt           fail            1
C               attempt           fail            0
D               attempt           fail            0

需求规则 #

如果某个Id的任意一行满足 condition1 == 'attempt' 且 condition2 == 'pass'，则该Id下所有行的score设为1，其余行保持0。

解决方案 #

用Pandas的筛选+映射即可实现，代码如下：

import pandas as pd

# 初始化示例数据
df = pd.DataFrame({
    'Id': ['A', 'A', 'B', 'B', 'B', 'C', 'D'],
    'condition1': ['attempt'] * 7,
    'condition2': ['pass', 'fail', 'pass', 'level_1', 'fail', 'fail', 'fail'],
    'score': [0] * 7
})

# 提取符合条件的唯一Id集合
qualified_ids = df[(df['condition1'] == 'attempt') & (df['condition2'] == 'pass')]['Id'].unique()

# 更新score列
df['score'] = df['Id'].isin(qualified_ids).astype(int)

# 输出结果
print(df)

代码解释 #

1. 筛选达标ID：通过布尔索引找出所有满足条件的行，提取唯一Id存入qualified_ids
2. 批量更新score：用isin()判断每行Id是否在达标集合中，返回布尔序列后转成整数（True→1，False→0），直接覆盖原score列

内容的提问来源于stack exchange，提问作者Roshankumar