import pandas as pd
import numpy as np
from pandas import Series
from pandas import DataFrame

l1 = [{'name': 'John', 'job': "teacher"},
      {'name': 'Nate', 'job': "student"},
      {'name': 'Fred', 'job': "developer"}]

l2 = [{'name': 'Ed', 'job': "dentist"},
      {'name': 'Jack', 'job': "farmer"},
      {'name': 'Ted', 'job': "designer"}]
         
df1 = pd.DataFrame(l1, columns = ['name', 'job'])
df2 = pd.DataFrame(l2, columns = ['name', 'job'])

row로 합치기

  1. concat([ ]) : 리스트 형식으로 데이터프레임 넣어주기
result = pd.concat([df1,df2])
result
   name        job
0  John    teacher
1  Nate    student
2  Fred  developer
0    Ed    dentist
1  Jack     farmer
2   Ted   designer

ignore_index = True를 활용해 인덱스 값을 맞출 수 있다.

result = pd.concat([df1,df2], ignore_index = True)
result
   name        job
0  John    teacher
1  Nate    student
2  Fred  developer
3    Ed    dentist
4  Jack     farmer
5   Ted   designer
  1. append()
result = df1.append(df2)
result
   name        job
0  John    teacher
1  Nate    student
2  Fred  developer
0    Ed    dentist
1  Jack     farmer
2   Ted   designer

마찬가지로 인덱스를 맞출 수 있다.

result = df1.append(df2, ignore_index = True)
result
   name        job
0  John    teacher
1  Nate    student
2  Fred  developer
3    Ed    dentist
4  Jack     farmer
5   Ted   designer

column으로 합치기

import pandas as pd
import numpy as np
from pandas import Series
from pandas import DataFrame

l1 = [{'name': 'John', 'job': "teacher"},
      {'name': 'Nate', 'job': "student"},
      {'name': 'Jack', 'job': "developer"}]

l2 = [{'age': 25, 'country': "U.S"},
      {'age': 30, 'country': "U.K"},
      {'age': 45, 'country': "Korea"}]
         
df1 = pd.DataFrame(l1, columns = ['name', 'job'])
df2 = pd.DataFrame(l2, columns = ['age', 'country'])