索引的不同选择

Object selection has had a number of user-requested additions in order to support more explicit location based indexing. Pandas now supports three types of multi-axis indexing.

• .loc is primarily label based, but may also be used with a boolean array. .loc will raise KeyError when the items are not found. Allowed inputs are:

  • A single label, e.g. 5 or ‘a’ (Note that 5 is interpreted as a label of the index. This use is not an integer position along the index.).
  • A list or array of labels [‘a’, ‘b’, ‘c’].
  • A slice object with labels ‘a’:’f’ (Note that contrary to usual python slices, both the start and the stop are included, when present in the index! See Slicing with labels.).
  • A boolean array
  • A callable function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing (one of the above). New in version 0.18.1.

  See more at Selection by Label.

• .iloc is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. .iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing. (this conforms with Python/NumPy slice semantics). Allowed inputs are:

  • An integer e.g. 5.
  • A list or array of integers [4, 3, 0].
  • A slice object with ints 1:7.
  • A boolean array.
  • A callable function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing (one of the above). New in version 0.18.1.

  See more at Selection by Position, Advanced Indexing and Advanced Hierarchical.

  • .loc, .iloc, and also [] indexing can accept a callable as indexer. See more at Selection By Callable.

Getting values from an object with multi-axes selection uses the following notation (using .loc as an example, but the following applies to .iloc as well). Any of the axes accessors may be the null slice :. Axes left out of the specification are assumed to be :, e.g. p.loc['a'] is equivalent to p.loc['a', :, :].