Python Pandas 非等式连接

Question

Have table

import pandas as pd
import numpy as np

list_1=[['Steven',np.nan,'C1'],
        ['Michael',np.nan,'C2'],
        ['Robert',np.nan,'C3'],
        ['Buchanan',np.nan,'C1'],
        ['Suyama',np.nan,'C2'],
        ['King',np.nan,'C3']]
labels=['first_name','last_name','class']
df=pd.DataFrame(list_1,columns=labels)
df

OUT

    first_name  last_name   class
0   Steven       NaN         C1
1   Michael      NaN         C2
2   Robert       NaN         C3
3   Buchanan     NaN         C1
4   Suyama       NaN         C2
5   King         NaN         C3

Need:

first_name  last_name
Steven       Buchanan
Michael      Suyama
Robert       King

so i need make non equal join equivalent SQL query :

;with cte as
(
SELECT first_name,
        class,
        ROW_NUMBER() OVER (partition by class ORDER BY first_name) as rn
FROM students
)
select c_fn.first_name,
        c_ln.first_name
from cte c_fn join cte c_ln on c_fn.class=c_ln.class and c_ln.rn< c_fn.rn

or as SQL query:

;with cte as
(
SELECT first_name,
        last_name,
        ROW_NUMBER() OVER ( ORDER BY (select null)) as rn
FROM students
)
select fn.first_name,
        ln.first_name as last_name
from cte fn join cte ln on ln.rn=fn.rn+3

The problem in PANDAS is that NON EQUAL SELF JOIN cannot be done with MERGE. And I can't find another way.....

Answer 1

We can solve this in pandas in a smarter way by using groupby with agg and joining the strings. Then we split them to columns:

dfn = df.groupby('class').agg(' '.join)['first_name'].str.split(' ', expand=True)
dfn.columns = [df.columns[:2]]
dfn = dfn.reset_index(drop=True)

  first_name last_name
0     Steven  Buchanan
1    Michael    Suyama
2     Robert      King

Answer 2

You could set the index to 'class' and select the individual names:

df = df.setIndex('class')
first_name = df.loc["C1", "first_name"].values[0]
last_name = df.loc["C1", "last_name"].values[1]