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

maketuplebool: 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])

numpy.r_ numpy.nonzero