一个选项是使用groupby：

# Get rows equal to ('J', 33)
check = df.loc(axis=1)[['Name','Age']].eq(('J', 33)).all(axis=1)
# run a groupby and get groups where True exists for any row in that group
check = check.groupby([df.X, df.Y]).transform('any')
#filter original dataframe
df.loc[check]

   X  Y Name  Age
0  1  3    J   33
1  1  3    A   47
4  2  3    J   33
5  2  3    P   80

另一个选项，仍然使用groupby：

group = df.groupby(['X','Y'])
cond1 = group.Name.transform(lambda x: any(x == 'J'))
cond2 = group.Age.transform(lambda x: any(x == 33))
df.loc[cond1 & cond2]
   X  Y Name  Age
0  1  3    J   33
1  1  3    A   47
4  2  3    J   33
5  2  3    P   80

您可以搜索所有包含Name == J和Age == 33的唯一X和Y值，然后过滤 Dataframe ：

mask = (df['Name'] == 'J') & (df['Age'] == 33)

unique_x = df.loc[mask, 'X'].unique()
unique_y = df.loc[mask, 'Y'].unique()

print(df[df['X'].isin(unique_x) & df['Y'].isin(unique_y)])

图纸：

X  Y Name  Age
0  1  3    J   33
1  1  3    A   47
4  2  3    J   33
5  2  3    P   80

编辑：更新问题，没有.groupby的解决方案：

mask = (df['Name'] == 'J') & (df['Age'] == 33)

t = set(df.loc[mask, ['X', 'Y']].drop_duplicates().apply(tuple, 1))
out = df[df.loc[:, ['X', 'Y']].apply(lambda x: tuple(x) in t, axis=1)]

print(out)

图纸：

X  Y Name  Age
0  1  3    J   33
1  1  3    A   47
4  2  3    J   33
5  2  3    P   80