{"id":6016,"date":"2018-01-17T10:32:48","date_gmt":"2018-01-17T05:02:48","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=6016"},"modified":"2026-04-28T14:41:53","modified_gmt":"2026-04-28T09:11:53","slug":"python-generator","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/python-generator\/","title":{"rendered":"Python Generator – Python Generator Expressions"},"content":{"rendered":"

In our last Python Tutorial, we studied Python functions. Today, in this Python Generator tutorial, we will study what a generator in Python programming is.<\/p>\n

Along with this, we will discuss Python Generator Expressions, Python list vs generator, and Python Function vs Generators.<\/p>\n

So, let’s start the Python Generator Tutorial.<\/p>\n

\"Python<\/a>

Python Generator – Python Generator Expressions (Best Lesson)<\/p><\/div>\n

Define Python Generator?<\/h3>\n

A Python generator is a kind of iterable, like a Python list or a Python tuple. It generates for us a sequence of values that we can iterate on.<\/p>\n

You can use it to iterate on a for-loop in Python, but you can\u2019t index it. Let\u2019s take a look at how to create one with a Python generator example.<\/p>\n

Function of a generator in Python:<\/strong><\/p>\n

    \n
  • Saves memory:<\/strong> It saves the system from slowing down as it calculates the next value only if required.<\/li>\n
  • Knows where it left:<\/strong> When a generator yield statement is being hit, it pauses the code, and when it resumes, it starts from the part where it was stopped.<\/li>\n
  • Infinite data:<\/strong> Since it creates one data point at a time, the code will run while having a lot of space in it.<\/li>\n<\/ul>\n

    The Syntax of Generator in Python 3<\/h3>\n

    To create a Python generator, we use the yield statement, inside a function, instead of the return statement. Let\u2019s take a quick example.<\/p>\n

    >>> def counter():\r\n     i=1\r\n     while(i<=10):\r\n          yield i\r\n          i+=1<\/pre>\n
    With this, we defined a Python generator called counter() and assigned 1 to the local variable i. As long as i is less than or equal to 10, the loop will execute.<\/p>\n
    Inside the loop, we yield the value of I, and then increment it.<\/p>\n
    Then, we iterate on this generator using the for-loop.<\/p>\n
    >>> for i in counter():\r\n         print(i)<\/pre>\n
    Output<\/strong><\/p>\n
    1
    \n2
    \n3
    \n4
    \n5
    \n6
    \n7
    \n8
    \n9
    \n10<\/div>\n
    Working of Python Generator<\/h3>\n
    To understand how this code works, we\u2019ll start with the for-loop. For each item in the Python generator (each item that it yields), it prints it, here.<\/p>\n
    We begin with i=1. So, the first item that it yields is 1. The for-loop prints this because of our print statement. Then, we increment I to 2.<\/p>\n
    And the process follows until i is incremented to 11. Then, the while loop\u2019s condition becomes False.<\/p>\n
    However, if you forget the statement to increment I, it results in an infinite generator. This is because a Python generator needs to hold only one value at a time.<\/p>\n
    So, there are no memory restrictions.<\/p>\n
    >>> def even(x):\r\n  while x%2==0:\r\n    yield 'Even'\r\n>>> for i in even(2):\r\n  print(i)<\/pre>\n
    Output<\/strong><\/p>\n
    Even
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEven
    \nEvenEvenTraceback (most recent call last):File “<pyshell#24>”, line 2, in <module>print(i)KeyboardInterrupt<\/div>\n
    Here, since 2 is even, 2%2 is always 0. Hence, the condition for while is always true.<\/p>\n
    Because of this, the Python3 generator even() keeps yielding the value True until we hit Ctrl+C on the keyboard to interrupt the execution.<\/p>\n
    Note that a generator may contain more than one Python yield statement. This is comparable to how a Python generator function may contain more than one return statement.<\/p>\n
    >>> def my_gen(x):\r\n  while(x>0):\r\n    if x%2==0:\r\n      yield 'Even'\r\n    else:\r\n      yield 'Odd'\r\n    x-=1\r\n>>> for i in my_gen(7):\r\n  print(i)<\/pre>\n
    Output<\/strong><\/p>\n
    Odd
    \nEven
    \nOdd
    \nEven
    \nOdd
    \nEven
    \nOdd<\/div>\n
    Yielding into a Python List<\/h3>\n
    This one\u2019s a no-brainer. If you apply the list() function to the call to the Python generator, it will return a list of the yielded values, in the order in which they are yielded.<\/p>\n
    Here, we take an example that creates a list of squares of numbers, on the condition that the squares are even.<\/p>\n
    >>> def even_squares(x):\r\n  for i in range(x):\r\n    if i**2%2==0:\r\n      yield i**2<\/pre>\n
    To create a list, we just apply the list() function to the call to this Python generator. We do not iterate on it using a for-loop.<\/p>\n
    >>> print(list(even_squares(10)))<\/pre>\n
    Output<\/strong><\/p>\n
    [0, 4, 16, 36, 64]<\/div>\n
    As you can see, in numbers 1 to 9 (not 10, because range(10) gives us 0 to 9), the even squares are 0, 4, 16, 36, and 64. The others, that are 1, 9, 25, 49, 81 are odd. So, they\u2019re not yielded.<\/p>\n
    Python List vs Generator in Python<\/h3>\n
    This is a very simple difference. A list holds a number of values at once. But a Python generator holds only one value at a time, the value to yield.<\/p>\n
    This is why it needs much less space compared to a list. With a generator, we also don\u2019t need to wait until all the values are rendered.<\/p>\n
    Python Generator vs Function<\/h3>\n
    Now, to compare a generator to a function, we first talk about return and Python yield.<\/p>\n
    When the interpreter reaches the return statement in a function, it stops executing the Python Generator function and executes the statement after the function call.<\/p>\n
    However, when it reaches the Python yield statement in a generator, it yields the value to the iterable. Finally, it gets back to the generator to continue for the next value.<\/p>\n
    Also, when a function stops executing, its local variables are destroyed. This is not the same with a Python generator. Take a look.<\/p>\n
    >>> def mygen():\r\n  i=7\r\n  while i>0:\r\n    yield i\r\n    i-=1\r\n>>> for i in mygen():\r\n  print(i)<\/pre>\n
    Output<\/strong><\/p>\n
    7
    \n6
    \n5
    \n4
    \n3
    \n2
    \n1<\/div>\n
    Python Generator Expressions<\/h3>\n
    Just like a list comprehension, we can use expressions to create Python generator shorthand. Let\u2019s take a list for this.<\/p>\n
    >>> mylist=[1,3,6,10]\r\n>>> (x**2 for x in mylist)<\/pre>\n
    Output<\/strong><\/p>\n
    <generator object <genexpr> at 0x003CC330><\/div>\n
    As is visible, this gave us a Python generator object. But to access the values, we need to store this into a variable, and then apply the next() function to it.<\/p>\n
    >>> a=(x**2 for x in mylist)\r\n>>> next(a)<\/pre>\n
    Output<\/strong><\/p>\n
    1<\/div>\n
    >>> next(a)<\/pre>\n
    Output<\/strong><\/p>\n
    9<\/div>\n
    >>> next(a)<\/pre>\n
    Output<\/strong><\/p>\n
    36<\/div>\n
    >>> next(a)<\/pre>\n
    Output<\/strong><\/p>\n
    100<\/div>\n
    >>> next(a)<\/pre>\n
    Output<\/strong><\/p>\n
    \n
    Traceback (most recent call last):File “<pyshell#89>”, line 1, in <module><\/p>\n
    next(a)<\/p>\n
    StopIteration<\/p>\n<\/div>\n
    So, this was all about Python Generator Tutorial. Hope you like our explanation.<\/p>\n
    Python Interview Questions on Generators<\/h3>\n
      \n
    1. What are generators in Python?<\/li>\n
    2. Why are generators used in Python?<\/li>\n
    3. What is the difference between generators and iterators in Python?<\/li>\n
    4. Which keyword is used for a generator in Python?<\/li>\n
    5. How is a generator function recognized in Python?<\/li>\n<\/ol>\n
      Conclusion<\/h3>\n
      Now that you know the benefits of Python generators over a list or over a function, you will understand their importance.<\/p>\n
      Some things we can do with a generator, with a function, or even with a list comprehension. But using a Python generator is the most efficient.<\/p>\n","protected":false},"excerpt":{"rendered":"
      In our last Python Tutorial, we studied Python functions. Today, in this Python Generator tutorial, we will study what a generator in Python programming is. Along with this, we will discuss Python Generator Expressions,...<\/p>\n","protected":false},"author":5,"featured_media":26384,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[46],"tags":[5049,5050,10550,10554,10556,10648,10933],"class_list":["post-6016","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","tag-generators-expressions-in-python","tag-generators-in-python","tag-python-function-vs-generator","tag-python-generator-functions","tag-python-generators","tag-python-list-vs-generator","tag-python-yield"],"yoast_head":"\nPython Generator - Python Generator Expressions - DataFlair<\/title>\n<meta name=\"description\" content=\"Python generator Tutorial -Python yield, generator expressions in python, difference between generator vs list in python, python generator vs function\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/python-generator\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python Generator - 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