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Slice semantics potentially differ per object; new semantics can be introduced when operator overloading the indexing operator. With Python standard lists (which are dynamic arrays), every slice is a copy. Slices of NumPy arrays, by contrast, are views onto the same underlying buffer.
last – the index of the last element in the slice end – one more than the index of last element in the slice len – the length of the slice (= end - first)
Python has array index and array slicing expressions in lists, denoted as a[key], a [start: stop] or a [start: stop: step]. Indexes are zero-based, and negative indexes are relative to the end. Slices take elements from the start index up to, but not including, the stop index.
The possible slicings depend on the implementation details: for example, Fortran allows slicing off one column of a matrix variable, but not a row, and treat it as a vector; whereas C allow slicing off a row from a matrix, but not a column. On the other hand, other slicing operations are possible when array types are implemented in other ways.
Based on the original definition of Weiser, [3] informally, a static program slice S consists of all statements in program P that may affect the value of variable v in a statement x. The slice is defined for a slicing criterion C=(x,v) where x is a statement in program P and v is variable in x.
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Despite the name, Python decorators are not an implementation of the decorator pattern. The decorator pattern is a design pattern used in statically-typed object-oriented programming languages to allow functionality to be added to objects at run time; Python decorators add functionality to functions and methods at definition time, and thus are ...