布尔索引

Another common operation is the use of boolean vectors to filter the data. The operators are: | for or, & for and, and ~ for not. These must be grouped by using parentheses, since by default Python will evaluate an expression such as df.A > 2 & df.B < 3 as df.A > (2 & df.B) < 3, while the desired evaluation order is (df.A > 2) & (df.B < 3).

Using a boolean vector to index a Series works exactly as in a NumPy ndarray:

  1. In [147]: s = pd.Series(range(-3, 4))
  3. In [148]: s
  4. Out[148]: 
  5. 0   -3
  6. 1   -2
  7. 2   -1
  8. 3    0
  9. 4    1
  10. 5    2
  11. 6    3
  12. dtype: int64
  14. In [149]: s[s > 0]
  15. Out[149]: 
  16. 4    1
  17. 5    2
  18. 6    3
  19. dtype: int64
  21. In [150]: s[(s < -1) | (s > 0.5)]
  22. Out[150]: 
  23. 0   -3
  24. 1   -2
  25. 4    1
  26. 5    2
  27. 6    3
  28. dtype: int64
  30. In [151]: s[~(s < 0)]
  31. Out[151]: 
  32. 3    0
  33. 4    1
  34. 5    2
  35. 6    3
  36. dtype: int64

You may select rows from a DataFrame using a boolean vector the same length as the DataFrame’s index (for example, something derived from one of the columns of the DataFrame):

  1. In [152]: df[df['A'] > 0]
  2. Out[152]: 
  3.                    A         B         C         D   E   0
  4. 2000-01-01  0.469112 -0.282863 -1.509059 -1.135632 NaN NaN
  5. 2000-01-02  1.212112 -0.173215  0.119209 -1.044236 NaN NaN
  6. 2000-01-04  7.000000 -0.706771 -1.039575  0.271860 NaN NaN
  7. 2000-01-07  0.404705  0.577046 -1.715002 -1.039268 NaN NaN

List comprehensions and map method of Series can also be used to produce more complex criteria:

  1. In [153]: df2 = pd.DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'],
  2.    .....:                     'b' : ['x', 'y', 'y', 'x', 'y', 'x', 'x'],
  3.    .....:                     'c' : np.random.randn(7)})
  4.    .....: 
  6. # only want 'two' or 'three'
  7. In [154]: criterion = df2['a'].map(lambda x: x.startswith('t'))
  9. In [155]: df2[criterion]
  10. Out[155]: 
  11.        a  b         c
  12. 2    two  y  0.041290
  13. 3  three  x  0.361719
  14. 4    two  y -0.238075
  16. # equivalent but slower
  17. In [156]: df2[[x.startswith('t') for x in df2['a']]]
  18. Out[156]: 
  19.        a  b         c
  20. 2    two  y  0.041290
  21. 3  three  x  0.361719
  22. 4    two  y -0.238075
  24. # Multiple criteria
  25. In [157]: df2[criterion & (df2['b'] == 'x')]
  26. Out[157]: 
  27.        a  b         c
  28. 3  three  x  0.361719

With the choice methods Selection by Label, Selection by Position, and Advanced Indexing you may select along more than one axis using boolean vectors combined with other indexing expressions.

  1. In [158]: df2.loc[criterion & (df2['b'] == 'x'),'b':'c']
  2. Out[158]: 
  3.    b         c
  4. 3  x  0.361719