注解代码

# frequency table of user login count per date
s = pd.crosstab(df['User'], df['Date'])

# remove the date columns where user has
# logged in more than one time in a given day
s = s.loc[:, ~(s > 1).any()]

# select the rows where user has logged in for all dates
s = s.loc[s.eq(1).all(1), :]

# filter/select the rows where User satisfy's the above condition
df.query('User in @s.index')

结果

Date    User
2  01/03/23  Andrew
3  02/03/23  Andrew
5  01/02/23  Andrew

以下是使用drop_duplicates()和nunique()的选项

#remove all duplicates if both Date and User are duplicated
df2 = df.drop_duplicates(keep=False)

#groupby user and find the unique count to compare to total unique count
df2.loc[df2.groupby('User')['Date'].transform('nunique').eq(df2['Date'].nunique())]

输出：

Date    User
2  01/03/23  Andrew
3  02/03/23  Andrew
5  01/02/23  Andrew