Python~pandas中关于set_index和reset_index的用法

1.set_index

DataFrame可以通过set_index方法，可以设置单索引和复合索引。 
DataFrame.set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False) 
append添加新索引，drop为False，inplace为True时，索引将会还原为列

1. In [307]: data

2. Out[307]:

3. a b c d

4. 0 bar one z 1.0

5. 1 bar two y 2.0

6. 2 foo one x 3.0

7. 3 foo two w 4.0

8.  

9. In [308]: indexed1 = data.set_index('c')

10.  

11. In [309]: indexed1

12. Out[309]:

13. a b d

14. c

15. z bar one 1.0

16. y bar two 2.0

17. x foo one 3.0

18. w foo two 4.0

19.  

20. In [310]: indexed2 = data.set_index(['a', 'b'])

21.  

22. In [311]: indexed2

23. Out[311]:

24. c d

25. a b

26. bar one z 1.0

27. two y 2.0

28. foo one x 3.0

29. two w 4.0

2.reset_index

reset_index可以还原索引，从新变为默认的整型索引 
DataFrame.reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill=”) 
level控制了具体要还原的那个等级的索引 
drop为False则索引列会被还原为普通列，否则会丢失

1. In [318]: data

2. Out[318]:

3. c d

4. a b

5. bar one z 1.0

6. two y 2.0

7. foo one x 3.0

8. two w 4.0

9.  

10. In [319]: data.reset_index()

11. Out[319]:

12. a b c d

13. 0 bar one z 1.0

14. 1 bar two y 2.0

15. 2 foo one x 3.0

16. 3 foo two w 4.0