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Introduction to Python Generators

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Why do we Need Python Generators?

There are several reasons in order to make/Create a Python Generator. They are Easy Implementation, Memory Efficiency, Infinite Stream representation, and Generators Pipelining.

Easy Implementation: Python Generators are implemented in an easy and clear, concise way as compared to the iterator class’s counterpart.

Memory Efficiency: This will be a sequence that is memory friendly because it will only iterate/produce only one item at a time, whereas a normal function with the return will return the sequence, which creates the whole sequence in the memory before providing the result. So the Python Generator Function is widely preferred compared to the normal function creation because the normal function’s sequences memory is so large than many Function/Functions.

Infinite Stream Representation: Representing the infinite stream of vast data need an excellent and great medium, and that medium is nothing but the Functions.

Generators Pipelining: Python Generator Function/Functions are used in creating the connection to the series of operations like a pipeline, so it is called Pipelining Generators/Generators Pipelining. It’s like connecting the series of the sequences one by one, just like the iteration.


def function(): List of statements Yield statement Variable= sum(function)

Explanation: The above syntax creates a function with some list of statements and the yield statement whenever required. Using the yield term inside of the normal function makes the function as Python Generator Function/Python Generator. Then a variable is created and assigned with the sum(function) value. One can also use the print in order to know what is the value of the sum() function’s value which is stored in the variable.

How to Create Python Generators?

Creating Python Generator/Generator Function is very simple with the help of the “yield” statement and the normal function. The yield statement is used instead of the “return” statement.

If the “yield” statement is used in a normal function, then the function is called a generator function. Yield is similar to the return function. These “yield” and “return” will provide mostly the same value from the function. The “yield” statement usually pauses the normal function by saving all of its states, and later the functions will be continued from the remaining successive calls. Generator Function may contain many yield statements as required/needed.


It works by involving the two terms. Python Generator Function and Object. “yield” is the most important term, instead of the “return” terms in the normal function.

Function: Generator Function of the Python Language is defined just like the normal function, but when the result needs to be produced, the term “yield” will be used instead of the “return” term to generate value. If the def function’s body contains the yield word, then the whole function becomes a Generator Function of the python programming language.

Object: Usually, the Generator Function will return the Generator Object. The Object is used usually to call the method (next method), or it is used by placing the generator object in the “FOR IN” LOOP.

Examples Example #1

The below example is to yield/ return 5 numbers from 101 to 105. At first, a function “EasysimpleGeneratorFun1” is created using the def() function, which yields/returns the value “101” at first, then 102 for the second time, then 103 for the third time, 104 for the fourth time, and then at last 105 will be yielded at last. In order to check the above function’s yielding code using a driving code (which is nothing but the “FOR LOOP”).


def EasysimpleGeneratorFun1(): yield 101 yield 102 yield 103 yield 104 yield 105 for value01 in EasysimpleGeneratorFun1(): print(value01)


Example #2

In practical, integers actually don’t even take more space for storing purposes, but if the integer is too big, the storage capacity will be somewhat less when we compare with the normal function, which has the return function to get the result. Python programming language provides the generator functions in order to build the iterators in shortcuts. The below code is fully acceptable but in the Python 2 version firstnaturalsum() is equivalent to xrange() (function) which is built-in function. Likewise, in the Python 3 version, the range() function is the immutable type sequence. In general, built-in functions are always faster than all the other functions.


def firstnaturalsum(n1): numone = 0 while numone <= n1: yield numone numone += 1 sum_of_first_natural = sum(firstnaturalsum(4)) print (sum_of_first_natural);


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This is a guide to Python Generators. Here we discuss Why we need, How to create, and Working on Python Generators with some examples in order to know well about the Functions. You can also go through our other related articles to learn more –

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