日期时间

For datetime64[ns] types, NaT represents missing values. This is a pseudo-native sentinel value that can be represented by NumPy in a singular dtype (datetime64[ns]). pandas objects provide intercompatibility between NaT and NaN.

  1. In [14]: df2 = df.copy()
  3. In [15]: df2['timestamp'] = pd.Timestamp('20120101')
  5. In [16]: df2
  6. Out[16]: 
  7.         one       two     three four   five  timestamp
  8. a -0.166778  0.501113 -0.355322  bar  False 2012-01-01
  9. c -0.337890  0.580967  0.983801  bar  False 2012-01-01
  10. e  0.057802  0.761948 -0.712964  bar   True 2012-01-01
  11. f -0.443160 -0.974602  1.047704  bar  False 2012-01-01
  12. h -0.717852 -1.053898 -0.019369  bar  False 2012-01-01
  14. In [17]: df2.loc[['a','c','h'],['one','timestamp']] = np.nan
  16. In [18]: df2
  17. Out[18]: 
  18.         one       two     three four   five  timestamp
  19. a       NaN  0.501113 -0.355322  bar  False        NaT
  20. c       NaN  0.580967  0.983801  bar  False        NaT
  21. e  0.057802  0.761948 -0.712964  bar   True 2012-01-01
  22. f -0.443160 -0.974602  1.047704  bar  False 2012-01-01
  23. h       NaN -1.053898 -0.019369  bar  False        NaT
  25. In [19]: df2.get_dtype_counts()
  26. Out[19]: 
  27. float64           3
  28. object            1
  29. bool              1
  30. datetime64[ns]    1
  31. dtype: int64