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Python supports most object oriented programming (OOP) techniques. It allows polymorphism, not only within a class hierarchy but also by duck typing. Any object can be used for any type, and it will work so long as it has the proper methods and attributes. And everything in Python is an object, including classes, functions, numbers and modules.
[26] [27] In C++, an abstract class is a class having at least one abstract method given by the appropriate syntax in that language (a pure virtual function in C++ parlance). [25] A class consisting of only pure virtual methods is called a pure abstract base class (or pure ABC) in C++ and is also known as an interface by users of the language. [13]
Before version 3.0, Python had two kinds of classes (both using the same syntax): old-style and new-style; [113] current Python versions only support the semantics of the new style. Python supports optional type annotations. [4] [114] These annotations are not enforced by the language, but may be used by external tools such as mypy to catch errors.
If the function is only used once, or a limited number of times, an anonymous function may be syntactically lighter than using a named function. Anonymous functions are ubiquitous in functional programming languages and other languages with first-class functions , where they fulfil the same role for the function type as literals do for other ...
A function definition starts with the name of the type of value that it returns or void to indicate that it does not return a value. This is followed by the function name, formal arguments in parentheses, and body lines in braces. In C++, a function declared in a class (as non-static) is called a member function or method.
The only difference in implementation is that in the first case we used a nested function with a name, g, while in the second case we used an anonymous nested function (using the Python keyword lambda for creating an anonymous function). The original name, if any, used in defining them is irrelevant.
This is a list of well-known data structures. For a wider list of terms, see list of terms relating to algorithms and data structures. For a comparison of running times for a subset of this list see comparison of data structures.
In Python 3.x the range() function [28] returns a generator which computes elements of the list on demand. Elements are only generated when they are needed (e.g., when print(r[3]) is evaluated in the following example), so this is an example of lazy or deferred evaluation: >>>