With generators, you can mass produce a particular sequence of values without storing them entirely in memory. Furthermore, the “generator” function creates an object that produces values when iterated. So, if you want to get your hands on a Python generator, this tutorial is for you. Here, we will explain how to create and use a Python generator.
How to Create and Use Python Generators
Python generators are used to effectively operate on data sequences, primarily when working with large or nearly endless datasets. The benefits of Python generators are:
- Short: You can easily define generators in a concise manner which improves code readability.
- Memory efficient: They together generate a single value which consumes less memory and improves efficiency.
- Built-in functions: In Python, generators have predefined functions like next(), iter(), and yield to make their work easier.
- Pause and resume feature: When working on complex algorithms, you can use the “Yield” function to pause and resume the operation of the generator.
You can create and use generators in a variety of ways in Python. We will further break down this section to demonstrate its implementation using various examples. First, let's take a look at the basic syntax:
def func_name,, ,
Yield Expression
“Diff” defines a function, and “yield” is used to create a “generator” function. However, another method also helps to create generators using only one-line expressions.
Generator function to generate a string
Let's define a generator function to get some value:
def parent,,,
Yield “It's a generator”
For price In parent,,,
printing,price,
When we repeat this using the “for” loop, the program produces the specified value which you can print using the “print” function.
Generator function to create a counter
The following program is an example of a generator function to generate a sequence of numbers:
def my_generator,n,,
counter , 0
Whereas counter < n:
Yield counter
counter+, 1
For counter In my_generator,10,,
printing,counter,
For example, if the input is 10, you will get values from 0 to 9 when you compile this code.
Fibonacci Series Using Generator Function
Let's now use the Fibonacci series which is the most basic series in programming:
I, J , 0, 1
Whereas I < Limit:
Yield I
I, J , J, i + j
A , series_fibonacci,6,
printing,next,A,,
printing,next,A,,
printing,next,A,,
printing,next,A,,
printing,next,A,,
printing,next,A,,
By running the code we will get the series as follows:
First, create an object called “A” and call the “generator” function with the desired inputs as arguments.
The next() keyword is a manual iterator. Every time we use next(a), it iterates over one value of “a”. However, we can use a “for” loop to print all the values of “a” at once as shown in the following example:
Using a “for” loop instead of the next() function:
I, J , 0, 1
Whereas I < Limit:
Yield I
I, J , J, i + j
A , series_fibonacci,6,
For price In A :
printing,price,
The result of running the code will not differ as it is an alternative way of writing the previous program:
What are generator expressions in Python?
Python generator expressions are a way to create concise “generator” functions using linear expressions. Here is the simple syntax:
,Expression For variables In recyclable If Situation,
For example, create a generator expression to calculate the squares of odd numbers from 0 to “n” when “n” is the input value.
odd_class , ,x*x For x In Category,10, If x% 2 , 0,
For price In odd_class:
printing,price,
The previous code gives the following result:
conclusion
That's all about different ways to create and use Python generators. We explained everything about Python generator expressions. If you consider using generators in any of your projects then you should use these expressions as they are more efficient in every programming aspect.
