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Lazy evaluation can also lead to reduction in memory footprint, since values are created when needed. [19] In practice, lazy evaluation may cause significant performance issues compared to eager evaluation. For example, on modern computer architectures, delaying a computation and performing it later is slower than performing it immediately.
Python functions decorated with Dask delayed adopt a lazy evaluation strategy by deferring execution and generating a task graph with the function and its arguments. The Python function will only execute when .compute is invoked. Dask delayed can be used as a function dask.delayed or as a decorator @dask.delayed.
Python uses the following syntax to express list comprehensions over finite lists: S = [ 2 * x for x in range ( 100 ) if x ** 2 > 3 ] A generator expression may be used in Python versions >= 2.4 which gives lazy evaluation over its input, and can be used with generators to iterate over 'infinite' input such as the count generator function which ...
Strict programming languages are often associated with eager evaluation, and non-strict languages with lazy evaluation, but other evaluation strategies are possible in each case. [ citation needed ] The terms "eager programming language" and "lazy programming language" are often used as synonyms for "strict programming language" and "non-strict ...
Lazy evaluation Typing Abstract data types Algebraic data types Data is immutable Type classes Garbage collection First appeared Common Lisp: No [1] Simulated with thunks [2] Dynamic [3] Yes [4] Extension [5] No [6]? Yes: 1984 Scheme: No [7] Yes [8] Dynamic [7] Yes [9] Simulated with thunks [10] No [11] No: Yes: 1975 Racket: No: Default in Lazy ...
In computer programming, lazy initialization is the tactic of delaying the creation of an object, the calculation of a value, or some other expensive process until the first time it is needed. It is a kind of lazy evaluation that refers specifically to the instantiation of objects or other resources.
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:
A lazy future is a future that deterministically has lazy evaluation semantics: the computation of the future's value starts when the value is first needed, as in call by need. Lazy futures are of use in languages which evaluation strategy is by default not lazy.