按列合并

（效果和增加列相同）

  1. df4 = pd.DataFrame({'address':['school','home','school','school','home']})
  2. df5 = pd.concat([df2,df4],axis=1)
  3. print('合并前的df2\n',df2)
  4. print('合并前的df4\n',df4)
  5. print('合并后的df5\n',df5)
  1. 合并前的df2
  2.       name   no  age gender  new_Col
  3. 0     Tom  001   16      m        1
  4. 1    Lily  002   16      f        2
  5. 2   Cindy  003   15      f        3
  6. 3  Petter  004   16      m        4
  7. 4   Stark  005   15      m        5
  8. 合并前的df4
  9.    address
  10. 0  school
  11. 1    home
  12. 2  school
  13. 3  school
  14. 4    home
  15. 合并后的df5
  16.       name   no  age gender  new_Col address
  17. 0     Tom  001   16      m        1  school
  18. 1    Lily  002   16      f        2    home
  19. 2   Cindy  003   15      f        3  school
  20. 3  Petter  004   16      m        4  school
  21. 4   Stark  005   15      m        5    home

按行合并

效果和增加学生信息相同

  1. df6 = pd.DataFrame({'name':['Tony'],'no':['005'],'age':[16],'gender':['m']})
  2. df7 = pd.concat([df1,df6],axis=0)
  3. print('合并前的df1\n',df1)
  4. print('合并前的df6\n',df6)
  5. print('合并后的df7\n',df7)
  1. 合并前的df1
  2.        name   no  age gender
  3. id                         
  4. 0      Tom  001   16      m
  5. 1     Lily  002   16      f
  6. 2    Cindy  003   15      f
  7. 3   Petter  004   16      m
  8. 合并前的df6
  9.     name   no  age gender
  10. 0  Tony  005   16      m
  11. 合并后的df7
  12.       name   no  age gender
  13. 0     Tom  001   16      m
  14. 1    Lily  002   16      f
  15. 2   Cindy  003   15      f
  16. 3  Petter  004   16      m
  17. 0    Tony  005   16      m