Lazy Evolution in Python
In this tutorial, we will learn about lazy evolution in Python and discuss how much Python optimized the code for us. We will also learn to write the Lazy function/classes.
Lazy evolution is a technique that delays evaluating an expression until its value is actually needed. It can be very useful where computing the value of an expression is time-consuming, and the value may only be needed in some cases. It is a strategy for optimizing the code. For example - We have a simple expression sum=2+3; Python will understand the expression and get the result as sum = 5; this calculation process is known as evolution. In this case, evolution is done immediately; we can also call it strict evolution.
On the other hand, the non-strict evolution is also known as lazy evolution. The main difference is the Lazy evolution will not immediately evaluate the expression but evaluate when we need the result. For example - A lazy student only completes his assignment when it needs to be submitted.
However, in programming being lazy is not harmful in programming. It can improve the efficiency of the code and save many resources. Python's many built-in methods come with lazy evolution, which optimizes the code. You must be familiar with most of the methods without knowing about lazy evolution.
Lazy Evaluation Functions
Let's see the lazily evaluated functions in Python.
Python typically evaluates expressions immediately, but there may be situations where lazy evaluation can be beneficial for optimizing code and improving performance. For example, consider the following code snippet - how long do you anticipate it will take to execute?
The given code snippet creates a list in Python, which causes all of the elements inside the list to be immediately evaluated. This can result in a longer execution time, even if only a small subset of the elements is actually needed. In other words, Python does not utilize lazy evaluation in this scenario, which can impact performance. It may be beneficial to use the lazy evaluation technique to improve performance and optimize code.
Python3 allows to traverse lists more memory-efficient and time-efficient using the range() function. In Python2, the range(5) would return a list of 5 elements. As the size of the list increases, more memory is used.
However, range(5) returns a range type, and this object can be iterated over to yield a sequence of numbers. One of the key benefits of using range() is that the size of the resulting object remains constant, regardless of the size of the range.
It is because range() does not actually store all of the integers in memory. Instead, it only stores the start, stop, and step values and computes each integer in the sequence as needed. This approach is sometimes referred to as lazy evaluation or on-demand computation.
Iterators and generators are related concepts in Python, but there are some important differences between them. In general, an iterator is a more general concept than a generator, and can be thought of as an object that generates a sequence of values.
In Python, an iterator is an object that implements the __next__() and __iter__() methods. The __next__() method returns the next value in the sequence, while __iter__() returns the iterator object itself. When an iterator is exhausted and no more values are generated, it raises the StopIteration exception.
On the other hand, a generator is a specific type of iterator created using a function. A generator function looks like a normal function, but it uses the yield keyword instead of using return to return a value. Each time the yield keyword is encountered, the function returns the current value and saves its state to resume from that point later.
Generators are a powerful tool for generating sequences of values in a memory-efficient and computationally efficient way. By using lazy evaluation and only generating values as needed, generators can handle very large sequences of data without requiring a large amount of memory.
A zip() method merges two iterables and returns the sequence of tuples. Let's see the following example.
The open() method is used to open the file normally. It doesn't read the entire file and is stored in the memory; instead, it returns a file object that can be iterated over. It can read the huge file with having large memory.
The x = map(lambda x:x*2, [1, 2, 3, 4, 5]) doesn't take any space in the memory. But when we do the list(x), it will print all the values and take space in the memory.
The map object is also a lazy object that can be iterated over. The computation x*2 will be done for only 1 item in each loop. When you do list(x), you basically compute all the values at one time. If you just want to iterate over the map object, you don't have to do list(x).
How to write a Lazy Evaluation function/class
As we have discussed that a main part of Lazy Evaluation is nothing more than a generator. We can write a function as a generator.
Lazy Function - Generator
This tutorial includes the basic concept of lazy evaluation and how it works. Lazy evaluation can be a powerful technique for optimizing code and improving performance, but it is only sometimes necessary or appropriate. It is important to consider the trade-offs and potential benefits carefully before implementing lazy evaluation in your code.
JavaTpoint offers too many high quality services. Mail us on h[email protected], to get more information about given services.
JavaTpoint offers college campus training on Core Java, Advance Java, .Net, Android, Hadoop, PHP, Web Technology and Python. Please mail your requirement at [email protected].
Duration: 1 week to 2 week