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Basically, a generator is a function that returns an object(iterator) which we can iterate over."/>

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<br>

<h3>Python Generators</h3>

<hr>

<p>In this tutorial, we will learn about generators in python,how they are different with iterators and normal functions and use of generators.</p>

<h4>Generators in Python</h4>

<p>As we know to build an iterator we have to use <b>__iter__()</b> and <b>__next__()</b> method.And we also use <b>StopIteration</b> to prevent the function to infinity.<br>

But these methods are bit lengthy.<b>Generator</b> comes here to prevent this and helps to rescue from these situations.</p>

<p><b>Generators</b> can be defined as a simple way of creating <b>iterators.</b><br>

Basically, a <b>generator</b> is a function that returns an object(iterator) which we can iterate over.</p>

<h5>How to create Generators in python?</h5>

<p>It is very easy to create generators in python.It is similar to defining a normal function but with a <b>yield</b> statement instead of <b>return</b> statement.<br>The difference between the <b>yield</b> and <b>return</b> statement is that <b>return</b> statement terminates the entire function in one time while <b>yield</b> statement to return each element one at a time.After running one time, it pauses the function and saves all its states and later continues for next calls.</p>

<h5>Main points about Generator function</h5>

<ul>

<li>Generator function is the best replacement for he lengthy definitions of iterators.</li>

<li>When generator function called,it return an iterator but it will not start the execution immediately.</li>

<li>Generator functions contains at least one <b>yield</b> statement.It can also cintain more than one <b>yield</b> statement.</li>

<li>Methods like <b>__iter__()</b> and <b>__next__()</b> called automatically with the help of generators.</li>

<li>When the function comes to terminates,<b>StopIteration</b> is raised automatically on further calls.</li>

</ul>

<h5>Python generators with loops</h5>

<p>In ths section we will learn about how generators can be used with loops.<br>

The generator functions are implemented within a lopp under suitable terminating condition.</p>

<b>Example:</b>

<pre><code>def rev_list(a=[]):

length = len(a)

for i in range(length - 1, -1, -1):

yield a[i]

# For loop to reverse the string

for word in rev_list(["hello","welcome","to","my","program"]):

print(word)

</code></pre>

<b>Output:</b>

<pre><code>program

welcome

hello

</code></pre>

<p>In the above program we used generator that reverses a list and here we used range() function to get the index of the reverse order of the list.</p>

<h5>Python Generator Expression</h5>

<p>Generators can be easily created by using generator expressions.It makes building generator very easy.<br>

Generator expressions create anonymous generator functions like <b>lambda</b> functions which are used to create anonymous functions.<br>

</p>

<b>Syntax:</b>

<p>The syntax for generator expression is similar to that of a list comprehension in Python. But the square brackets are replaced with round parentheses.</p>

<b>Example:</b>

<pre><code># Initialize the list

my_list = [1,5,25,100]

# multiply each term by 2 using list comprehension

list_1 = [var*2 for var in my_list]

# same thing can be done using a generator expression

# generator expressions are surrounded by parenthesis ()

generator_1 = (var*2 for var in my_list)

print(list_1)

print(generator_1)

print(next(generator_1))

</code></pre>

<b>Output:</b>

<pre><code>[2, 6, 12, 20]

</code></pre>

<p>As you can see the above code will return a generator object which produces items only on demand.<br>

To produce the result we have to pass the generator in <b>next statement.</b>.</p>

<h5>Use of Python Generators</h5>

<ol><li>Easy Implementation</li>

<p>Generators can be implemented in very simple and concise way as compared to the iterator.<br>

Let's take an example, we will create a simple program by using iterator as well as generator.</p>

<b>Iterator example:</b>

<pre><code>class Multi:

def __init__(self, mix=0):

self.n = 0

self.mix = mix

def __iter__(self):

return self

def __next__(self):

if self.n < self.min:

raise StopIteration

result = 2 * self.n

self.n += 1

return result

</code></pre>

<b>Generator example:</b>

<pre><code>def MultiGen(min=0):

n = 0

while n < min:

yield 2 * n

n += 1

</code></pre>

<li>Memory Efficient</li>

<p>A normal function to return a sequence will create the entire sequence in memory before returning the result. <br>

Generator implementation of such sequences is memory friendly and is preferred since it only produces one item at a time.</p>

</ol>

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