切片范围

The most robust and consistent way of slicing ranges along arbitrary axes is described in the Selection by Position section detailing the .iloc method. For now, we explain the semantics of slicing using the [] operator.

With Series, the syntax works exactly as with an ndarray, returning a slice of the values and the corresponding labels:

  1. In [31]: s[:5]
  2. Out[31]: 
  3. 2000-01-01    0.469112
  4. 2000-01-02    1.212112
  5. 2000-01-03   -0.861849
  6. 2000-01-04    0.721555
  7. 2000-01-05   -0.424972
  8. Freq: D, Name: A, dtype: float64
  10. In [32]: s[::2]
  11. Out[32]: 
  12. 2000-01-01    0.469112
  13. 2000-01-03   -0.861849
  14. 2000-01-05   -0.424972
  15. 2000-01-07    0.404705
  16. Freq: 2D, Name: A, dtype: float64
  18. In [33]: s[::-1]
  19. Out[33]: 
  20. 2000-01-08   -0.370647
  21. 2000-01-07    0.404705
  22. 2000-01-06   -0.673690
  23. 2000-01-05   -0.424972
  24. 2000-01-04    0.721555
  25. 2000-01-03   -0.861849
  26. 2000-01-02    1.212112
  27. 2000-01-01    0.469112
  28. Freq: -1D, Name: A, dtype: float64

Note that setting works as well:

  1. In [34]: s2 = s.copy()
  3. In [35]: s2[:5] = 0
  5. In [36]: s2
  6. Out[36]: 
  7. 2000-01-01    0.000000
  8. 2000-01-02    0.000000
  9. 2000-01-03    0.000000
  10. 2000-01-04    0.000000
  11. 2000-01-05    0.000000
  12. 2000-01-06   -0.673690
  13. 2000-01-07    0.404705
  14. 2000-01-08   -0.370647
  15. Freq: D, Name: A, dtype: float64

With DataFrame, slicing inside of [] slices the rows. This is provided largely as a convenience since it is such a common operation.

  1. In [37]: df[:3]
  2. Out[37]: 
  3.                    A         B         C         D
  4. 2000-01-01  0.469112 -0.282863 -1.509059 -1.135632
  5. 2000-01-02  1.212112 -0.173215  0.119209 -1.044236
  6. 2000-01-03 -0.861849 -2.104569 -0.494929  1.071804
  8. In [38]: df[::-1]
  9. Out[38]: 
  10.                    A         B         C         D
  11. 2000-01-08 -0.370647 -1.157892 -1.344312  0.844885
  12. 2000-01-07  0.404705  0.577046 -1.715002 -1.039268
  13. 2000-01-06 -0.673690  0.113648 -1.478427  0.524988
  14. 2000-01-05 -0.424972  0.567020  0.276232 -1.087401
  15. 2000-01-04  0.721555 -0.706771 -1.039575  0.271860
  16. 2000-01-03 -0.861849 -2.104569 -0.494929  1.071804
  17. 2000-01-02  1.212112 -0.173215  0.119209 -1.044236
  18. 2000-01-01  0.469112 -0.282863 -1.509059 -1.135632