使用isin进行索引

Consider the isin() method of Series, which returns a boolean vector that is true wherever the Series elements exist in the passed list. This allows you to select rows where one or more columns have values you want:

  1. In [159]: s = pd.Series(np.arange(5), index=np.arange(5)[::-1], dtype='int64')
  3. In [160]: s
  4. Out[160]: 
  5. 4    0
  6. 3    1
  7. 2    2
  8. 1    3
  9. 0    4
  10. dtype: int64
  12. In [161]: s.isin([2, 4, 6])
  13. Out[161]: 
  14. 4    False
  15. 3    False
  16. 2     True
  17. 1    False
  18. 0     True
  19. dtype: bool
  21. In [162]: s[s.isin([2, 4, 6])]
  22. Out[162]: 
  23. 2    2
  24. 0    4
  25. dtype: int64

The same method is available for Index objects and is useful for the cases when you don’t know which of the sought labels are in fact present:

  1. In [163]: s[s.index.isin([2, 4, 6])]
  2. Out[163]: 
  3. 4    0
  4. 2    2
  5. dtype: int64
  7. # compare it to the following
  8. In [164]: s.reindex([2, 4, 6])
  9. Out[164]: 
  10. 2    2.0
  11. 4    0.0
  12. 6    NaN
  13. dtype: float64

In addition to that, MultiIndex allows selecting a separate level to use in the membership check:

  1. In [165]: s_mi = pd.Series(np.arange(6),
  2.    .....:                  index=pd.MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]))
  3.    .....: 
  5. In [166]: s_mi
  6. Out[166]: 
  7. 0  a    0
  8.    b    1
  9.    c    2
  10. 1  a    3
  11.    b    4
  12.    c    5
  13. dtype: int64
  15. In [167]: s_mi.iloc[s_mi.index.isin([(1, 'a'), (2, 'b'), (0, 'c')])]
  16. Out[167]: 
  17. 0  c    2
  18. 1  a    3
  19. dtype: int64
  21. In [168]: s_mi.iloc[s_mi.index.isin(['a', 'c', 'e'], level=1)]
  22. Out[168]: 
  23. 0  a    0
  24.    c    2
  25. 1  a    3
  26.    c    5
  27. dtype: int64

DataFrame also has an isin() method. When calling isin, pass a set of values as either an array or dict. If values is an array, isin returns a DataFrame of booleans that is the same shape as the original DataFrame, with True wherever the element is in the sequence of values.

  1. In [169]: df = pd.DataFrame({'vals': [1, 2, 3, 4], 'ids': ['a', 'b', 'f', 'n'],
  2.    .....:                    'ids2': ['a', 'n', 'c', 'n']})
  3.    .....: 
  5. In [170]: values = ['a', 'b', 1, 3]
  7. In [171]: df.isin(values)
  8. Out[171]: 
  9.     vals    ids   ids2
  10. 0   True   True   True
  11. 1  False   True  False
  12. 2   True  False  False
  13. 3  False  False  False

Oftentimes you’ll want to match certain values with certain columns. Just make values a dict where the key is the column, and the value is a list of items you want to check for.

  1. In [172]: values = {'ids': ['a', 'b'], 'vals': [1, 3]}
  3. In [173]: df.isin(values)
  4. Out[173]: 
  5.     vals    ids   ids2
  6. 0   True   True  False
  7. 1  False   True  False
  8. 2   True  False  False
  9. 3  False  False  False

Combine DataFrame’s isin with the any() and all() methods to quickly select subsets of your data that meet a given criteria. To select a row where each column meets its own criterion:

  1. In [174]: values = {'ids': ['a', 'b'], 'ids2': ['a', 'c'], 'vals': [1, 3]}
  3. In [175]: row_mask = df.isin(values).all(1)
  5. In [176]: df[row_mask]
  6. Out[176]: 
  7.    vals ids ids2
  8. 0     1   a    a