基本说明

As mentioned when introducing the data structures in the last section, the primary function of indexing with [] (a.k.a. __getitem__ for those familiar with implementing class behavior in Python) is selecting out lower-dimensional slices. The following table shows return type values when indexing pandas objects with []:

Object Type | Selection | Return Value Type Series | series[label] | scalar value DataFrame | frame[colname] | Series corresponding to colname Panel | panel[itemname] | DataFrame corresponding to the itemname

Here we construct a simple time series data set to use for illustrating the indexing functionality:

  1. In [1]: dates = pd.date_range('1/1/2000', periods=8)
  3. In [2]: df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
  5. In [3]: df
  6. Out[3]: 
  7.                    A         B         C         D
  8. 2000-01-01  0.469112 -0.282863 -1.509059 -1.135632
  9. 2000-01-02  1.212112 -0.173215  0.119209 -1.044236
  10. 2000-01-03 -0.861849 -2.104569 -0.494929  1.071804
  11. 2000-01-04  0.721555 -0.706771 -1.039575  0.271860
  12. 2000-01-05 -0.424972  0.567020  0.276232 -1.087401
  13. 2000-01-06 -0.673690  0.113648 -1.478427  0.524988
  14. 2000-01-07  0.404705  0.577046 -1.715002 -1.039268
  15. 2000-01-08 -0.370647 -1.157892 -1.344312  0.844885
  17. In [4]: panel = pd.Panel({'one' : df, 'two' : df - df.mean()})
  19. In [5]: panel
  20. Out[5]: 
  21. <class 'pandas.core.panel.Panel'>
  22. Dimensions: 2 (items) x 8 (major_axis) x 4 (minor_axis)
  23. Items axis: one to two
  24. Major_axis axis: 2000-01-01 00:00:00 to 2000-01-08 00:00:00
  25. Minor_axis axis: A to D

Note: None of the indexing functionality is time series specific unless specifically stated.

Thus, as per above, we have the most basic indexing using []:

  1. In [6]: s = df['A']
  3. In [7]: s[dates[5]]
  4. Out[7]: -0.67368970808837059
  6. In [8]: panel['two']
  7. Out[8]: 
  8.                    A         B         C         D
  9. 2000-01-01  0.409571  0.113086 -0.610826 -0.936507
  10. 2000-01-02  1.152571  0.222735  1.017442 -0.845111
  11. 2000-01-03 -0.921390 -1.708620  0.403304  1.270929
  12. 2000-01-04  0.662014 -0.310822 -0.141342  0.470985
  13. 2000-01-05 -0.484513  0.962970  1.174465 -0.888276
  14. 2000-01-06 -0.733231  0.509598 -0.580194  0.724113
  15. 2000-01-07  0.345164  0.972995 -0.816769 -0.840143
  16. 2000-01-08 -0.430188 -0.761943 -0.446079  1.044010

You can pass a list of columns to [] to select columns in that order. If a column is not contained in the DataFrame, an exception will be raised. Multiple columns can also be set in this manner:

  1. In [9]: df
  2. Out[9]: 
  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
  7. 2000-01-04  0.721555 -0.706771 -1.039575  0.271860
  8. 2000-01-05 -0.424972  0.567020  0.276232 -1.087401
  9. 2000-01-06 -0.673690  0.113648 -1.478427  0.524988
  10. 2000-01-07  0.404705  0.577046 -1.715002 -1.039268
  11. 2000-01-08 -0.370647 -1.157892 -1.344312  0.844885
  13. In [10]: df[['B', 'A']] = df[['A', 'B']]
  15. In [11]: df
  16. Out[11]: 
  17.                    A         B         C         D
  18. 2000-01-01 -0.282863  0.469112 -1.509059 -1.135632
  19. 2000-01-02 -0.173215  1.212112  0.119209 -1.044236
  20. 2000-01-03 -2.104569 -0.861849 -0.494929  1.071804
  21. 2000-01-04 -0.706771  0.721555 -1.039575  0.271860
  22. 2000-01-05  0.567020 -0.424972  0.276232 -1.087401
  23. 2000-01-06  0.113648 -0.673690 -1.478427  0.524988
  24. 2000-01-07  0.577046  0.404705 -1.715002 -1.039268
  25. 2000-01-08 -1.157892 -0.370647 -1.344312  0.844885

You may find this useful for applying a transform (in-place) to a subset of the columns.

警告

pandas aligns all AXES when setting Series and DataFrame from .loc, and .iloc. This will not modify df because the column alignment is before value assignment.

  2. In [12]: df[['A', 'B']]
  3. Out[12]: 
  4.                    A         B
  5. 2000-01-01 -0.282863  0.469112
  6. 2000-01-02 -0.173215  1.212112
  7. 2000-01-03 -2.104569 -0.861849
  8. 2000-01-04 -0.706771  0.721555
  9. 2000-01-05  0.567020 -0.424972
  10. 2000-01-06  0.113648 -0.673690
  11. 2000-01-07  0.577046  0.404705
  12. 2000-01-08 -1.157892 -0.370647
  13. In [13]: df.loc[:,['B', 'A']] = df[['A', 'B']]

The correct way to swap column values is by using raw values:

  2. In [15]: df.loc[:,['B', 'A']] = df[['A', 'B']].values