# numpy.s_

numpy.s_ = <numpy.lib.index_tricks.IndexExpression object>

  A nicer way to build up index tuples for arrays.

  Note

  Use one of the two predefined instances `index_exp` or `s_` rather than directly using IndexExpression.

  For any index combination, including slicing and axis insertion, `a[indices]` is the same as `a[np.index_exp[indices]]` for any array a. However, `np.index_exp[indices]` can be used anywhere in Python code and returns a tuple of slice objects that can be used in the construction of complex index expressions.

  Parameters
    maketuple bool

      If True, always returns a tuple.

  See also

  `index_exp`

    Predefined instance that always returns a tuple: index_exp = IndexExpression(maketuple=True).

  `s_`

    Predefined instance without tuple conversion: s_ = IndexExpression(maketuple=False).

  Notes

  You can do all this with slice() plus a few special objects, but there’s a lot to remember and this version is simpler because it uses the standard array indexing syntax.

  Examples

      >>> np.s_[2::2]
      slice(2, None, 2)
      >>> np.index_exp[2::2]
      (slice(2, None, 2),)

      >>> np.array([0, 1, 2, 3, 4])[np.s_[2::2]]
      array([2, 4])

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