快速获取和设置标量的值

Since indexing with [] must handle a lot of cases (single-label access, slicing, boolean indexing, etc.), it has a bit of overhead in order to figure out what you’re asking for. If you only want to access a scalar value, the fastest way is to use the at and iat methods, which are implemented on all of the data structures.

Similarly to loc, at provides label based scalar lookups, while, iat provides integer based lookups analogously to iloc

  1. In [140]: s.iat[5]
  2. Out[140]: 5
  4. In [141]: df.at[dates[5], 'A']
  5. Out[141]: -0.67368970808837059
  7. In [142]: df.iat[3, 0]
  8. Out[142]: 0.72155516224436689

You can also set using these same indexers.

  1. In [143]: df.at[dates[5], 'E'] = 7
  3. In [144]: df.iat[3, 0] = 7

at may enlarge the object in-place as above if the indexer is missing.

  1. In [145]: df.at[dates[-1]+1, 0] = 7
  3. In [146]: df
  4. Out[146]: 
  5.                    A         B         C         D    E    0
  6. 2000-01-01  0.469112 -0.282863 -1.509059 -1.135632  NaN  NaN
  7. 2000-01-02  1.212112 -0.173215  0.119209 -1.044236  NaN  NaN
  8. 2000-01-03 -0.861849 -2.104569 -0.494929  1.071804  NaN  NaN
  9. 2000-01-04  7.000000 -0.706771 -1.039575  0.271860  NaN  NaN
  10. 2000-01-05 -0.424972  0.567020  0.276232 -1.087401  NaN  NaN
  11. 2000-01-06 -0.673690  0.113648 -1.478427  0.524988  7.0  NaN
  12. 2000-01-07  0.404705  0.577046 -1.715002 -1.039268  NaN  NaN
  13. 2000-01-08 -0.370647 -1.157892 -1.344312  0.844885  NaN  NaN
  14. 2000-01-09       NaN       NaN       NaN       NaN  NaN  7.0