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Nullable types are a feature of some programming languages which allow a value to be set to the special value NULL instead of the usual possible values of the data type.In statically typed languages, a nullable type is an option type, [citation needed] while in dynamically typed languages (where values have types, but variables do not), equivalent behavior is provided by having a single null ...
In programming languages (especially functional programming languages) and type theory, an option type or maybe type is a polymorphic type that represents encapsulation of an optional value; e.g., it is used as the return type of functions which may or may not return a meaningful value when they are applied.
In object-oriented computer programming, a null object is an object with no referenced value or with defined neutral (null) behavior.The null object design pattern, which describes the uses of such objects and their behavior (or lack thereof), was first published as "Void Value" [1] and later in the Pattern Languages of Program Design book series as "Null Object".
In computer programming, specifically when using the imperative programming paradigm, an assertion is a predicate (a Boolean-valued function over the state space, usually expressed as a logical proposition using the variables of a program) connected to a point in the program, that always should evaluate to true at that point in code execution.
In object-oriented programming, the safe navigation operator (also known as optional chaining operator, safe call operator, null-conditional operator, null-propagation operator) is a binary operator that returns null if its first argument is null; otherwise it performs a dereferencing operation as specified by the second argument (typically an ...
Undefined null results are one particular pain point that many procedural languages don't provide specific tools for dealing with, requiring use of the null object pattern or checks to test for invalid values at each operation to handle undefined values. This causes bugs and makes it harder to build robust software that gracefully handles errors.
Introduced in Python 2.2 as an optional feature and finalized in version 2.3, generators are Python's mechanism for lazy evaluation of a function that would otherwise return a space-prohibitive or computationally intensive list. This is an example to lazily generate the prime numbers: