属性访问

You may access an index on a Series, column on a DataFrame, and an item on a Panel directly as an attribute:

  1. In [17]: sa = pd.Series([1,2,3],index=list('abc'))
  3. In [18]: dfa = df.copy()
  1. In [19]: sa.b
  2. Out[19]: 2
  4. In [20]: dfa.A
  5. Out[20]: 
  6. 2000-01-01    0.469112
  7. 2000-01-02    1.212112
  8. 2000-01-03   -0.861849
  9. 2000-01-04    0.721555
  10. 2000-01-05   -0.424972
  11. 2000-01-06   -0.673690
  12. 2000-01-07    0.404705
  13. 2000-01-08   -0.370647
  14. Freq: D, Name: A, dtype: float64
  16. In [21]: panel.one
  17. Out[21]: 
  18.                    A         B         C         D
  19. 2000-01-01  0.469112 -0.282863 -1.509059 -1.135632
  20. 2000-01-02  1.212112 -0.173215  0.119209 -1.044236
  21. 2000-01-03 -0.861849 -2.104569 -0.494929  1.071804
  22. 2000-01-04  0.721555 -0.706771 -1.039575  0.271860
  23. 2000-01-05 -0.424972  0.567020  0.276232 -1.087401
  24. 2000-01-06 -0.673690  0.113648 -1.478427  0.524988
  25. 2000-01-07  0.404705  0.577046 -1.715002 -1.039268
  26. 2000-01-08 -0.370647 -1.157892 -1.344312  0.844885
  1. In [22]: sa.a = 5
  3. In [23]: sa
  4. Out[23]: 
  5. a    5
  6. b    2
  7. c    3
  8. dtype: int64
  10. In [24]: dfa.A = list(range(len(dfa.index)))  # ok if A already exists
  12. In [25]: dfa
  13. Out[25]: 
  14.             A         B         C         D
  15. 2000-01-01  0 -0.282863 -1.509059 -1.135632
  16. 2000-01-02  1 -0.173215  0.119209 -1.044236
  17. 2000-01-03  2 -2.104569 -0.494929  1.071804
  18. 2000-01-04  3 -0.706771 -1.039575  0.271860
  19. 2000-01-05  4  0.567020  0.276232 -1.087401
  20. 2000-01-06  5  0.113648 -1.478427  0.524988
  21. 2000-01-07  6  0.577046 -1.715002 -1.039268
  22. 2000-01-08  7 -1.157892 -1.344312  0.844885
  24. In [26]: dfa['A'] = list(range(len(dfa.index)))  # use this form to create a new column
  26. In [27]: dfa
  27. Out[27]: 
  28.             A         B         C         D
  29. 2000-01-01  0 -0.282863 -1.509059 -1.135632
  30. 2000-01-02  1 -0.173215  0.119209 -1.044236
  31. 2000-01-03  2 -2.104569 -0.494929  1.071804
  32. 2000-01-04  3 -0.706771 -1.039575  0.271860
  33. 2000-01-05  4  0.567020  0.276232 -1.087401
  34. 2000-01-06  5  0.113648 -1.478427  0.524988
  35. 2000-01-07  6  0.577046 -1.715002 -1.039268
  36. 2000-01-08  7 -1.157892 -1.344312  0.844885

警告

  • You can use this access only if the index element is a valid Python identifier, e.g. s.1 is not allowed. See here for an explanation of valid identifiers.
  • The attribute will not be available if it conflicts with an existing method name, e.g. s.min is not allowed.
  • Similarly, the attribute will not be available if it conflicts with any of the following list: index, major_axis, minor_axis, items.
  • In any of these cases, standard indexing will still work, e.g. s[‘1’], s[‘min’], and s[‘index’] will access the corresponding element or column.

If you are using the IPython environment, you may also use tab-completion to see these accessible attributes.

You can also assign a dict to a row of a DataFrame:

  1. In [28]: x = pd.DataFrame({'x': [1, 2, 3], 'y': [3, 4, 5]})
  3. In [29]: x.iloc[1] = dict(x=9, y=99)
  5. In [30]: x
  6. Out[30]: 
  7.    x   y
  8. 0  1   3
  9. 1  9  99
  10. 2  3   5

You can use attribute access to modify an existing element of a Series or column of a DataFrame, but be careful; if you try to use attribute access to create a new column, it creates a new attribute rather than a new column. In 0.21.0 and later, this will raise a UserWarning:

  1. In[1]: df = pd.DataFrame({'one': [1., 2., 3.]})
  2. In[2]: df.two = [4, 5, 6]
  3. UserWarning: Pandas doesn't allow Series to be assigned into nonexistent columns - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute_access
  4. In[3]: df
  5. Out[3]:
  6.    one
  7. 0  1.0
  8. 1  2.0
  9. 2  3.0