In [1]:

    

import pandas as pd


    

In [2]:

    

s_org = pd.Series(['aaa@xxx.com', 'bbb@yyy.com', 'ccc@zzz.com', 'ddd'], index=['A', 'B', 'C', 'D'])
print(s_org)
print(type(s_org))


    

    




A    aaa@xxx.com
B    bbb@yyy.com
C    ccc@zzz.com
D            ddd
dtype: object
<class 'pandas.core.series.Series'>

In [3]:

    

s = s_org.str.split('@')
print(s)
print(type(s))


    

    




A    [aaa, xxx.com]
B    [bbb, yyy.com]
C    [ccc, zzz.com]
D             [ddd]
dtype: object
<class 'pandas.core.series.Series'>

In [4]:

    

df = s_org.str.split('@', expand=True)
print(df)
print(type(df))


    

    




     0        1
A  aaa  xxx.com
B  bbb  yyy.com
C  ccc  zzz.com
D  ddd     None
<class 'pandas.core.frame.DataFrame'>

In [5]:

    

df.columns = ['local', 'domain']
print(df)


    

    




  local   domain
A   aaa  xxx.com
B   bbb  yyy.com
C   ccc  zzz.com
D   ddd     None

In [6]:

    

print(df['domain'].str.split('.', expand=True))


    

    




      0     1
A   xxx   com
B   yyy   com
C   zzz   com
D  None  None

In [7]:

    

df2 = pd.concat([df, df['domain'].str.split('.', expand=True)], axis=1).drop('domain', axis=1)
print(df2)


    

    




  local     0     1
A   aaa   xxx   com
B   bbb   yyy   com
C   ccc   zzz   com
D   ddd  None  None

In [8]:

    

df3 = pd.concat([df['local'], df['domain'].str.split('.', expand=True)], axis=1)
print(df3)


    

    




  local     0     1
A   aaa   xxx   com
B   bbb   yyy   com
C   ccc   zzz   com
D   ddd  None  None

In [9]:

    

df3.rename(columns={0: 'second_LD', 1: 'TLD'}, inplace=True)
print(df3)


    

    




  local second_LD   TLD
A   aaa       xxx   com
B   bbb       yyy   com
C   ccc       zzz   com
D   ddd      None  None