按可调用性选择

New in version 0.18.1.

.loc, .iloc, and also [] indexing can accept a callable as indexer. The callable must be a function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing.

  1. In [88]: df1 = pd.DataFrame(np.random.randn(6, 4),
  2.    ....:                    index=list('abcdef'),
  3.    ....:                    columns=list('ABCD'))
  4.    ....: 
  6. In [89]: df1
  7. Out[89]: 
  8.           A         B         C         D
  9. a -0.023688  2.410179  1.450520  0.206053
  10. b -0.251905 -2.213588  1.063327  1.266143
  11. c  0.299368 -0.863838  0.408204 -1.048089
  12. d -0.025747 -0.988387  0.094055  1.262731
  13. e  1.289997  0.082423 -0.055758  0.536580
  14. f -0.489682  0.369374 -0.034571 -2.484478
  16. In [90]: df1.loc[lambda df: df.A > 0, :]
  17. Out[90]: 
  18.           A         B         C         D
  19. c  0.299368 -0.863838  0.408204 -1.048089
  20. e  1.289997  0.082423 -0.055758  0.536580
  22. In [91]: df1.loc[:, lambda df: ['A', 'B']]
  23. Out[91]: 
  24.           A         B
  25. a -0.023688  2.410179
  26. b -0.251905 -2.213588
  27. c  0.299368 -0.863838
  28. d -0.025747 -0.988387
  29. e  1.289997  0.082423
  30. f -0.489682  0.369374
  32. In [92]: df1.iloc[:, lambda df: [0, 1]]
  33. Out[92]: 
  34.           A         B
  35. a -0.023688  2.410179
  36. b -0.251905 -2.213588
  37. c  0.299368 -0.863838
  38. d -0.025747 -0.988387
  39. e  1.289997  0.082423
  40. f -0.489682  0.369374
  42. In [93]: df1[lambda df: df.columns[0]]
  43. Out[93]: 
  44. a   -0.023688
  45. b   -0.251905
  46. c    0.299368
  47. d   -0.025747
  48. e    1.289997
  49. f   -0.489682
  50. Name: A, dtype: float64

You can use callable indexing in Series.

  1. In [94]: df1.A.loc[lambda s: s > 0]
  2. Out[94]: 
  3. c    0.299368
  4. e    1.289997
  5. Name: A, dtype: float64

Using these methods / indexers, you can chain data selection operations without using temporary variable.

  1. In [95]: bb = pd.read_csv('data/baseball.csv', index_col='id')
  3. In [96]: (bb.groupby(['year', 'team']).sum()
  4.    ....:    .loc[lambda df: df.r > 100])
  5.    ....: 
  6. Out[96]: 
  7.            stint    g    ab    r    h  X2b  X3b  hr    rbi    sb   cs   bb     so   ibb   hbp    sh    sf  gidp
  8. year team                                                                                                      
  9. 2007 CIN       6  379   745  101  203   35    2  36  125.0  10.0  1.0  105  127.0  14.0   1.0   1.0  15.0  18.0
  10.      DET       5  301  1062  162  283   54    4  37  144.0  24.0  7.0   97  176.0   3.0  10.0   4.0   8.0  28.0
  11.      HOU       4  311   926  109  218   47    6  14   77.0  10.0  4.0   60  212.0   3.0   9.0  16.0   6.0  17.0
  12.      LAN      11  413  1021  153  293   61    3  36  154.0   7.0  5.0  114  141.0   8.0   9.0   3.0   8.0  29.0
  13.      NYN      13  622  1854  240  509  101    3  61  243.0  22.0  4.0  174  310.0  24.0  23.0  18.0  15.0  48.0
  14.      SFN       5  482  1305  198  337   67    6  40  171.0  26.0  7.0  235  188.0  51.0   8.0  16.0   6.0  41.0
  15.      TEX       2  198   729  115  200   40    4  28  115.0  21.0  4.0   73  140.0   4.0   5.0   2.0   8.0  16.0
  16.      TOR       4  459  1408  187  378   96    2  58  223.0   4.0  2.0  190  265.0  16.0  12.0   4.0  16.0  38.0