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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.
The following list contains syntax examples of how a range of element of an array can be accessed. In the following table: first – the index of the first element in the slice; 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)
This list of lists of lists is a list of articles that are lists of other list articles. Each of the pages linked here is an index to multiple lists on a topic.
Slice indexes may be omitted—for example, a [:] returns a copy of the entire list. Each element of a slice is a shallow copy. In Python, a distinction between expressions and statements is rigidly enforced, in contrast to languages such as Common Lisp, Scheme, or Ruby. This leads to duplicating some functionality. For example:
Python sets are very much like mathematical sets, and support operations like set intersection and union. Python also features a frozenset class for immutable sets, see Collection types. Dictionaries (class dict) are mutable mappings tying keys and corresponding values. Python has special syntax to create dictionaries ({key: value})
In Raku, a sister language to Perl, for must be used to traverse elements of a list (foreach is not allowed). The expression which denotes the collection to loop over is evaluated in list-context, but not flattened by default, and each item of the resulting list is, in turn, aliased to the loop variable(s). List literal example:
Investigators are trying to determine how a woman got past multiple security checkpoints this week at New York’s JFK International Airport and boarded a plane to Paris, apparently hiding in the ...
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