Question

我在“ nearest_beacon”列中具有以下重复值的数据框，但在“ vms_distance”列中具有不同的距离：

nearest_beacon  vms_distance associated
2890231      0.421313        vms
2890231      0.215785        vms
2890231      0.104256        vms*
4548780      0.486456        vms
4548780      0.468065        vms
4548780      0.337609        vms
4548780      0.363601        vms
4548780      0.210566        vms
4548780      0.197327        vms*
4548780      0.285390        vms
4548780      0.216443        vms
1221421      0.441454        vms
1221421      0.337533        vms*

我想确定每个“ nearest_beacon”值中一行（*）的“ associated”列，并在“ vms_distance”列中设置最小值，将“ associated”设置为“ vms”，其余设置为“ no_vms”。

预期结果：

nearest_beacon  vms_distance associated
2890231      0.421313        no_vms
2890231      0.215785        no_vms
2890231      0.104256        vms
4548780      0.486456        no_vms
4548780      0.468065        no_vms
4548780      0.337609        no_vms
4548780      0.363601        no_vms
4548780      0.210566        no_vms
4548780      0.197327        vms
4548780      0.285390        no_vms
4548780      0.216443        no_vms
1221421      0.441454        no_vms
1221421      0.337533        vms

Answer 1

将groupby与idxmin一起使用，然后通过loc分配回去

df.loc[df.groupby('nearest_beacon').vms_distance.idxmin(),'associated']='no vms'

Answer 2

尝试一下

public class ApplicationUser : IdentityUser
{
    public async Task<ClaimsIdentity> GenerateUserIdentityAsync(UserManager<ApplicationUser> manager, string authenticationType)
    {
       CookieAuthenticationOptions.AuthenticationType
       var userIdentity = await manager.CreateIdentityAsync(this, authenticationType);

       // Add custom user claims here
        userIdentity.AddClaim(new Claim("Code",this.Code));
       return userIdentity;
    }
    //My extended property
    public string Code { get; set; }
}

Answer 3

执行以下操作：

df['associated'] = 'non_vms'
df.loc[df.groupby('nearest_beacon')['vms_distance'].idxmin(), 'associated'] = 'vms'
df

输出：

    nearest_beacon  vms_distance    associated
0   2890231 0.421313    non_vms
1   2890231 0.215785    non_vms
2   2890231 0.104256    vms
3   4548780 0.486456    non_vms
4   4548780 0.468065    non_vms
5   4548780 0.337609    non_vms
6   4548780 0.363601    non_vms
7   4548780 0.210566    non_vms
8   4548780 0.197327    vms
9   4548780 0.285390    non_vms
10  4548780 0.216443    non_vms
11  1221421 0.441454    non_vms
12  1221421 0.337533    vms

如何根据第二列中的最小值（按第一列分组）确定列值？

3 个答案: