{"id":75862,"date":"2020-01-30T11:35:07","date_gmt":"2020-01-30T06:05:07","guid":{"rendered":"https:\/\/techvidvan.com\/tutorials\/?p=75862"},"modified":"2020-01-30T11:35:07","modified_gmt":"2020-01-30T06:05:07","slug":"python-generators","status":"publish","type":"post","link":"https:\/\/techvidvan.com\/tutorials\/python-generators\/","title":{"rendered":"Python Generators – A complete guide to create and use generators"},"content":{"rendered":"

In our Python Iterators <\/strong>article, we create our own iterators<\/strong>.<\/p>\n

Generators<\/strong> are also used to create functions<\/strong> that behave<\/strong> like iterators<\/strong>. It is a different approach<\/strong> to create iterators<\/strong>.<\/p>\n

People often misunderstood this topic as a hard one because it looks different<\/strong> than the usual one but in fact, it is very easy<\/strong>. This article will teach you about Python generators<\/strong>, how you can create<\/strong> and effectively use generators<\/strong> in your code<\/strong>.<\/p>\n

We will also see its applications<\/strong> so let\u2019s get started.<\/p>\n

 <\/p>\n

What are Python Generator Functions?<\/h3>\n

Generator functions<\/strong> are special kind of functions that returns<\/strong> an iterator<\/strong> and we can loop<\/strong> it through just like a list<\/strong>, to access<\/strong> the objects<\/strong> one at a time<\/strong>.<\/p>\n

Python generator functions are a simple<\/strong> way to create iterators<\/strong>. They solve the common problem of creating iterable<\/strong> objects<\/strong>. The traditional way was to create a class<\/strong> and then we have to implement __iter__()<\/strong> and __next__()<\/strong> methods.<\/p>\n

We also have to manage the internal state<\/strong> and raise<\/strong> the StopIteration<\/strong> exception<\/strong> when the generator ends<\/strong>.<\/p>\n

How to Create a Python Generator?<\/h3>\n

A Python generator<\/strong> is created<\/strong> like a normal function<\/strong> but we do not use<\/strong> the return statement<\/strong> in generators<\/strong>.<\/p>\n

For generating a value, we use the yield keyword<\/strong>. When we use the yield keyword<\/strong> inside a function, it automatically<\/strong> becomes a generator function<\/strong>.<\/p>\n

Let us understand the working of a generator with a simple generator<\/strong>.<\/p>\n

def mygen():\n    yield 10\n    yield 20\n    yield 40\nf\ngen = mygen()\n\nprint(next(gen))\nprint(next(gen))\nprint(next(gen))\nprint(next(gen))<\/pre>\n
Output:<\/strong><\/p>\n
10
\n20
\n40
\nTraceback (most recent call last):
\n\u00a0\u00a0<\/span>File\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 “C:\/Users\/Techvidvan\/AppData\/Local\/Programs\/Python\/Python38-32\/test.py”, line 11, in <module>
\n\u00a0\u00a0\u00a0\u00a0<\/span>print(next(gen))
\nStopIteration<\/div>\n
Here, the mygen()<\/strong> function is a generator function<\/strong> in which we used 3 yield statements<\/strong>.<\/p>\n
The next()<\/strong> function used on the generator<\/strong> object returns<\/strong> the value<\/strong> of the first yield statement<\/strong>. Again calling the next()<\/strong> will then return the value<\/strong> from the second function<\/strong> and so on. When it gets exhausted<\/strong> the generator<\/strong> function raises<\/strong> the StopIteration exception<\/strong>.<\/p>\n
We can also use the for loop<\/strong> for easy traversing<\/strong> the elements<\/strong>.<\/p>\n
def mygen():\n    n = 1\n    yield n\n    n+=2\n    yield n\n    n+=3\n    yield n\n    yield \"End\"\n\nfor n in mygen():\n    print(n)<\/pre>\n
Output:<\/strong><\/p>\n
1
\n3
\n6
\nEnd<\/div>\n
One thing to notice here is that when the generator yields<\/strong> the values it remembers the value<\/strong> of the variable<\/strong> n between<\/strong> each call.<\/p>\n
Python Generators with Loops<\/h3>\n
The above examples were simple, only for understanding the working<\/strong> of the generators<\/strong>. Now we will see generators<\/strong> with a loop<\/strong> that is more practically applicable<\/strong> for creating <\/strong>customized iterable objects<\/strong>. We can use for-loop<\/strong> to yield values<\/strong>.<\/p>\n
Let us generate a sequence<\/strong> of Fibonacci numbers<\/strong>:<\/p>\n
def fib_sequence(n):\n   a,b = 0,1\n   for i in range(n):\n       yield a\n       a,b = b,a+b\n\nfor i in fib_sequence(10):\n    print(i)<\/pre>\n
Output:<\/strong><\/p>\n
0
\n1
\n1
\n2
\n3
\n5
\n8
\n13
\n21
\n34<\/div>\n
Using Generators with List() Function<\/h3>\n
We can use the lits() function<\/strong> to yield<\/strong> all the values<\/strong> into a list<\/strong>. This is very handy<\/strong> sometimes. We can try this by using the same fib_sequence()<\/strong> generator function<\/strong> we made.<\/p>\n
print( list(fib_sequence(20)) )<\/pre>\n
Output:<\/strong><\/p>\n
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181]<\/div>\n
Python Generator Expressions<\/h3>\n
If you are familiar with list <\/strong>comprehensions<\/strong> then this would be very easy for you to understand. We have even a more shorthand technique<\/strong> to create python generators<\/strong>.<\/p>\n
In list comprehensions<\/strong> we use [] brackets<\/strong> while in generator expressions<\/strong> we use () parenthesis<\/strong>. They are used at places where we quickly want to use a generator<\/strong> right away.<\/p>\n
gen = (x**2 for x in range(10))\n\nfor x in gen:\n  print(x)<\/pre>\n
Output:<\/strong><\/p>\n
0
\n1
\n4
\n9
\n16
\n25
\n36
\n49
\n64
\n81<\/div>\n
Here, we created a generator that is used to generate<\/strong> a square<\/strong> of 0-9 numbers<\/strong>. If we print<\/strong> the gen object<\/strong> then we see that it is a generator object<\/strong>.<\/p>\n
print(gen)<\/pre>\n
Output:<\/strong><\/p>\n
<generator object <genexpr> at 0x05F3F7D0><\/div>\n
Why Generators are Used?<\/h3>\n
    \n
  • Generators are used to create iterable<\/strong> objects<\/strong>.<\/li>\n
  • They are easier to implement than the creation <\/strong>of iterable objects<\/strong> which we saw in the iterables article.<\/li>\n
  • The generators yield<\/strong> one value<\/strong> at a time from a set of items<\/strong>.<\/li>\n
  • So they are memory efficient<\/strong> as they require less space<\/strong> and we don\u2019t have to store everything at once.<\/li>\n
  • It is also easier to implement<\/strong> an infinite generation<\/strong> of series<\/strong>.<\/li>\n
  • We cannot store infinite series<\/strong> of a sequence in memory<\/strong> so with generators we can continuously generate values<\/strong> or infinite values(theoretically)<\/strong><\/li>\n
  • Generators are also helpful in reading<\/strong> and performing operations<\/strong> on large files<\/strong>.<\/li>\n
  • We can use generators<\/strong> to effectively<\/strong> write code<\/strong> for our programs<\/strong>.<\/li>\n<\/ul>\n
    Summary<\/h3>\n
    Python Generators<\/strong> are often misinterpreted<\/strong> as a hard topic but in fact, they are very useful and easy, once you start understanding how it works. We saw what Python generators are, how<\/strong> to create them<\/strong> and iterate over generators<\/strong> with for loops<\/strong>.<\/p>\n
    Moreover, we saw an even more easy way to create generators i.e. generators expression<\/strong>.<\/p>\n
    In the end, we saw the advantages<\/strong> of python generators.<\/p>\n","protected":false},"excerpt":{"rendered":"
    In our Python Iterators article, we create our own iterators. Generators are also used to create functions that behave like iterators. It is a different approach to create iterators. People often misunderstood this topic...<\/p>\n","protected":false},"author":1,"featured_media":76044,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1053],"tags":[1508,1509,1510,1511,1512,1513],"class_list":["post-75862","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","tag-generators-in-python","tag-how-to-create-a-python-generator","tag-python-generator-expressions","tag-python-generators","tag-python-generators-with-loops","tag-what-are-python-generator-functions"],"yoast_head":"\nPython Generators - A complete guide to create and use generators - TechVidvan<\/title>\n<meta name=\"description\" content=\"Learn to create generators in Python, its functions and expressions. 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