如何对比同名行并新增列标注唯一性原因？

原始DataFrame #

需求 #

• 当Name列存在重复值时，对比所有同名行，找出导致该行与其他同名行存在差异的列名，将这些列名用逗号分隔存入新增的Reason列
• 若某个Name仅出现一次，Reason列填写Unique

预期输出 #

尝试的代码（未得到预期结果） #

dif = [i for i, (x,y) in enumerate(zip(df.loc[0].values, df.loc[9,:].values)) if x!=y ]
df.iloc[:, dif]

解决方案 #

你当前的代码仅对比了第0行和第9行，没有按Name分组处理所有同名行，无法覆盖所有场景。以下是满足需求的实现方案：

完整代码 #

import pandas as pd

# 构造原始DataFrame
data = [
    ["A",23,"DE","Job holder","Fishing"],
    ["A",23,"DE","Job holder","Gardening"],
    ["A",23,"DE","Student","Fishing"],
    ["A",23,"DE","Job holder","Reading"],
    ["B",15,"SW","Job holder","Fishing"],
    ["B",15,"SW","Job holder","Playing"],
    ["C",23,"DD","Job holder","Coding"],
    ["B",23,"AA","Job holder","Fishing"],
    ["D",34,"GH","Job holder","Fishing"],
    ["D",33,"TR","Other","Fishing"]
]
df = pd.DataFrame(data, columns=["Name","Age","Country","Occupation","Hobby"])

# 定义分组处理函数
def get_reason(group):
    if len(group) == 1:
        return "Unique"
    # 筛选分组内存在不同值的列（排除Name列）
    diff_cols = [col for col in group.columns.drop("Name") if group[col].nunique() > 1]
    return ", ".join(diff_cols)

# 按Name分组处理，将结果映射到原DataFrame
df["Reason"] = df.groupby("Name").transform(get_reason)

print(df)

代码说明 #

• groupby("Name")：按Name列分组，确保只处理同名的行
• transform(get_reason)：将分组处理的结果广播到原DataFrame的每一行，保证同一Name的所有行Reason值一致
• nunique()：统计列内不同值的数量，大于1说明该列在分组内存在差异
• 排除Name列是因为分组依据就是Name，该列值完全相同，无需纳入差异判断

内容的提问来源于stack exchange，提问作者s_max