numpy.s_

numpy.s_ =

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:

Parameters:	maketuple : bool If True, always returns a tuple.

maketuple : bool

If True, always returns a tuple.

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])

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.s_.html

doc_NumPy

2025-01-10 15:47:30

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