如何用Python Pandas将用户组字典转换为Y/N校验DataFrame表格

实现代码（原生Pandas，无额外依赖） #

import pandas as pd

d = {
    'user1': ['group1', 'group2', 'group3'],
    'user2': ['group1', 'group2'],
    'user3': ['group2']
}

# 转换字典为Series，索引为用户名
user_series = pd.Series(d)
# 展开用户组列表为单行单组格式
exploded_series = user_series.explode()
# 生成独热编码后按用户名聚合，替换数值为Y/N
result_df = pd.get_dummies(exploded_series)\
              .groupby(level=0)\
              .sum()\
              .replace({1: 'Y', 0: 'N'})

print(result_df)

运行后输出内容如下：

group1 group2 group3
user1      Y      Y      Y
user2      Y      Y      N
user3      N      Y      N

可选精简实现（需安装scikit-learn） #

import pandas as pd
from sklearn.preprocessing import MultiLabelBinarizer

d = {
    'user1': ['group1', 'group2', 'group3'],
    'user2': ['group1', 'group2'],
    'user3': ['group2']
}

mlb = MultiLabelBinarizer()
result_df = pd.DataFrame(
    mlb.fit_transform(d.values()),
    index=d.keys(),
    columns=mlb.classes_
).replace({1: 'Y', 0: 'N'})

print(result_df)

内容的提问来源于stack exchange，提问作者ajd018