pandas group by and assign a group id then ungroup

Question

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, sn         


        

Answer 1

You can use sklearn.preprocessing.LabelEncoder method:

In [79]: from sklearn.preprocessing import LabelEncoder

In [80]: le = LabelEncoder()

In [81]: df['groupId'] = le.fit_transform(df['socialmedia'])+1

In [82]: df
Out[82]:
    id socialmedia  groupId
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

Answer 2

By using 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

---

Or pd.factorize and '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

Answer 3

We could also create a dictionary and map it:

import pandas as pd

df = pd.DataFrame(dict(id=range(1,5),social=["Facebook","Twitter","Facebook","Google"]))

d = dict((k,v) for v,k in enumerate(df['social'].unique(),1))
df['groupid'] = df['social'].map(m)

print(df)

Returns

   id    social  groupid
0   1  Facebook        1
1   2   Twitter        2
2   3  Facebook        1
3   4    Google        3

Or one-line like this:

df['groupid'] = df['social'].map({k:v for v,k in enumerate(df['social'].unique(),1)})

---

Timings:

%timeit df['grpId']=df.groupby('social').ngroup().add(1)
%timeit df['grpId']=pd.factorize(df['social'])[0]+1
%timeit df['grpId']=df['social'].astype('category').cat.codes.add(1)
%timeit df['groupid'] = df['social'].map(dict((k,v) for v,k in enumerate(df['social'].unique(),1)))

Returns

100 loops, best of 3: 1.5 ms per loop   <- Wen1
1000 loops, best of 3: 493 µs per loop  <- Wen2
1000 loops, best of 3: 990 µs per loop  <- Wen3
1000 loops, best of 3: 802 µs per loop  <- Antonvbr