pandas 分组依据并分配分组ID，然后取消分组 [英] pandas group by and assign a group id then ungroup

问题描述

我有以下格式的大数据集:

I have a large data set in the following format:

id, socialmedia
1, facebook
2, facebook
3, google
4, google
5, google
6, twitter
7, google
8, twitter
9, snapchat
10, twitter
11, facebook

我想在那时进行分组，并分配一个group_id列，然后取消分组(扩展)回各个记录.

I want to group by then and assign a group_id column and then ungroup (expand) back to individual records.

id, socialmedia, groupId
1, facebook, 1
2, facebook, 1
3, google, 2
4, google, 2
5, google, 2
6, twitter, 3
7, google, 2
8, twitter, 3
9, snapchat, 4
10, twitter, 3
11, facebook, 1

我尝试了以下操作，但最终以"DataFrameGroupBy"对象不支持项目分配.

I tried following but end up with 'DataFrameGroupBy' object does not support item assignment.

x['grpId'] = x.groupby('socialmedia')['socialmedia'].rank(method='dense').astype(int)

推荐答案

通过使用ngroup

df['grpId']=df.groupby(' socialmedia').ngroup().add(1)
df
Out[354]: 
    id  socialmedia  grpId
0    1     facebook      1
1    2     facebook      1
2    3       google      2
3    4       google      2
4    5       google      2
5    6      twitter      4
6    7       google      2
7    8      twitter      4
8    9     snapchat      3
9   10      twitter      4
10  11     facebook      1

---

或pd.factorize和'categroy'

df['grpId']=pd.factorize(df[' socialmedia'])[0]+1

df
Out[358]: 
    id  socialmedia  grpId
0    1     facebook      1
1    2     facebook      1
2    3       google      2
3    4       google      2
4    5       google      2
5    6      twitter      3
6    7       google      2
7    8      twitter      3
8    9     snapchat      4
9   10      twitter      3
10  11     facebook      1

---

df['grpId']=df[' socialmedia'].astype('category').cat.codes.add(1)
df
Out[356]: 
    id  socialmedia  grpId
0    1     facebook      1
1    2     facebook      1
2    3       google      2
3    4       google      2
4    5       google      2
5    6      twitter      4
6    7       google      2
7    8      twitter      4
8    9     snapchat      3
9   10      twitter      4
10  11     facebook      1

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