Iterators and generators

It is hard to move the common part to a function.For example.Iterators 5.Iterables which can iterate only once such as Generators customarily return this from their iterator method, whereas iterables which can be iterated many times must return a new iterator on each invocation of iterator.Question feed.Viewed k times.As mentioned, generators are an easier way to create iterables.Getting Started with NestJS.So from this we learn that Generators are a convenient type of Iterator.If you need more insight as to how Redux saga applies generators, let me know in the comments section below.Let’s call the function getTitle Now, in the function, we have to go through some steps to execute the generator Initially, we will call the generator method.It includes an operator called yield which allows pausing the generator function itself until the next value is requested.That generator then controls the execution of a generator function.Casual imprecision is fine but a precise, authoritative source should at least be one of the options on SO.Memory Efficient A direct outcome of Lazy Evaluation is that generators are memory efficient.Leave a Reply Cancel reply.For control flow matters, generators are an as much important concept as promises: both are abstract and composable.Every generator object Iterators and generators an iterator but not vice versa.New post Iterators and generators designs on greatest hits now, everywhere else eventually.
Difference between Python’s Generators and Iterators – Stack Overflow

Iterators and generators – I’m working in Apache Spark and it enforces a very functional programming style.About your first code snippet, I’d like to know what else arg ‘stream’ could be than the list[]? One Pythonist’s obscurity can be the center of another’s project design! A generator is a function that produces a sequence of results instead of a single value.They look like list comprehensions, but returns a generator back instead of a list.

Change Language.Related Articles.Table of Contents.Improve Article.Save Article.Like Article.Example: Python3.Previous How to add colour to text Python? Next Rotation of colorbar tick labels in Matplotlib.The Symbol.So, you could use it to retrieve a function that iterates over an array object, like so —.Generator functions once called, returns the Generator object, which holds the entire Generator iterable and can be iterated using next method.

Every next call on the generator executes every line of code until it encounters the next yield and suspends its execution temporarily.Generators are a special type of function in JavaScript that can pause and resume state.A Generator function returns an iterator, which can be used to stop the function in the middle, do something, and then resume it whenever.An async function can be decomposed into a generator and promise implementation which is good to know stuff.

Additionally, generators can also receive input and send output via yield.In short, a generator appears to be a function but it behaves like an iterator.A generator is a function that returns an object on which you can call next.Every invocation of next will return an object of shape —.

The value property will contain the value.The done property is either true or false.The yield is a magical keyword that can do more things other than simply return a value and next can do more things aside from retrieving the value.A passing argument to next – The argument passed to next will be received by yield —.In this example; to fetch data from API, we have to install node-fetch using the command — ‘ npm install node-fetch’.We then pass a generator to a function as a parameter.

Let’s call the function getTitle.Initially, we will call the generator method.It returns an iterator object which is caught in a variable ‘ iterator ‘.

When the next method is called, the generator starts executing from this point.At line 4 the ‘ URL ‘ is fetched.Active 1 month ago.Viewed k times.Add a comment.Active Oldest Votes.Alex Martelli Alex Martelli k gold badges silver badges bronze badges.Can you clarify what the correct lingo is here.I hear a lot of people using the term “Generator” interchangeably with “Generator Function” and “Generator Expression”, like in a Generator Function is a Generator and a Generator Expression is a Generator.

I am confused.A Generator is an Iterator Specifically, generator is a subtype of iterator.GeneratorType, collections.Iterator True We can create a generator several ways.Iterator, collections.Emphasis added.So from this we learn that Generators are a convenient type of Iterator.Example Iterator Objects You might create object that implements the Iterator protocol by creating or extending your own object.Maggyero 4, 3 3 gold badges 26 26 silver badges 46 46 bronze badges.

Iterators: Iterator are objects which uses next method to get next value of sequence.Generators: A generator is a function that produces or yields a sequence of values using yield method.Instead of f.The Python tutorial remarkably manages to imply both usages in the space of three sentences: Generators are a simple and powerful tool for creating iterators.

Despite all this confusion, one can seek out the Python language reference for the clear and final word: The yield expression is only used when defining a generator function, and can only be used in the body of a function definition.

However, the Python 3 glossary states that generator Community Bot 1 1 1 silver badge.Paul Paul 3, 1 1 gold badge 28 28 silver badges 41 41 bronze badges.I don’t think there’s much confusion between generator functions and generator objects, for the same reason there’s usually no confusion between classes and their instances.In both cases, you call one to get the other, and in casual conversation or quickly written documentation you can use the class name or the word “generator” for either one.

You only need to be explicit about “generator function” versus “generator object” in rare situations where which one you’re talking about matters.Regardless of theoretical reasons for why there shouldn’t be confusion, comments on other answers to this question deny and contradict one another without resolution, indicating actual confusion exists.

Casual imprecision is fine but a precise, authoritative source should at least be one of the options on SO.I use both generator functions and objects extensively in my current project, and the distinction is very important when designing and coding.It’s good to know what terminology to use now, so I don’t have to change dozens of variable names and comments later on.

Imagine a mathematics literature where no distinction is made between a function and its return value.It is occasionally convenient to conflate them informally, but it increases the risk of a variety of mistakes.Advanced modern mathematics would be significantly and needlessly hampered if the distinction were not formalized in convention, language, and notation.

Higher-order functions passing around generators or generator functions may sound weird, but for me they have been coming up.I’m working in Apache Spark and it enforces a very functional programming style.

The functions have to create, pass in, and pass out all sorts of objects to get things done.I’ve had a number of situations where I lost track of what kind of “generator” I was working with.Hints in variable names and comments, using the consistent and correct terminology, helped clear up the confusion.

One Pythonist’s obscurity can be the center of another’s project design! Paul, thanks for writing this answer.This confusion is important because the difference between a generator object and a generator function is the difference between getting the desired behavior and having to lookup generators.Show 2 more comments.

I just wanted to give a short few lines answer for people who are still not quite clear conceptually: If you create your own iterator, it is a little bit involved – you have to create a class and at least implement the iter and the next methods.This something has a name in Python called Generator Hope that clarifies a bit.Heapify Heapify 2, 14 14 silver badges 16 16 bronze badges.Marwan Mostafa Marwan Mostafa 2 2 silver badges 12 12 bronze badges.About your first code snippet, I’d like to know what else arg ‘stream’ could be than the list[]?

Hibou57 Hibou57 6, 4 4 gold badges 46 46 silver badges 50 50 bronze badges.Could you give an example to illustrate you mean when talking about composition?

Also, can you explain what you have in mind when talking about ” typical iterators”? Another answer stackoverflow.Please do not confuse standards for duck typing with their implementation how a particular Python interpreter implemented it.This is a bit like the confusion between generator functions definition and generator objects implementation.Generator Function, Generator Object, Generator: A Generator function is just like a regular function in Python but it contains one or more yield statements.

Iterator : Every generator object is an iterator but not vice versa.N Randhawa N Randhawa 7, 2 2 gold badges 40 40 silver badges 46 46 bronze badges.

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Generators simplifies creation of iterators.A generator is a function that produces a sequence of results instead of a single value.iterator function on an object is responsible for returning the list of values to iterate on.Iterable interface.Iterable is a type we can use if we want to.Every generator is an iterator, but not vice versa.A generator is built by calling a function that has one or more yield expressions (yield statements.

How to use:

  1. We send the resolved ‘ x’ to the iterator’s next method.
  2. Lazy Evaluation.
  3. However, in order to use an Iterator the value or data structure should be iterable.
  4. Start Your Coding Journey Now!
  5. Therefore, implementing iterables is one of the most important use cases of Generators.
Generators and Iterators in python – python tutorial, time: 14:00

JavaScript iterators and generators: A complete guide

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Difference Between Iterator VS Generator – properties

  • Hints in variable names and comments, using the consistent and correct terminology, helped clear up the confusion.
  • So, you could use it to retrieve a function that iterates over an array object, like so —.
  • As the name already gives away, iterators allow you to iterate over an object arrays are also objects.
  • But with generators makes it possible to do it.
  • Stay tuned to our Design Podcast Designwise.
  • The functions have to create, pass in, and pass out all sorts of objects to get things done.
  • Another answer stackoverflow.
  • Now we are going to extract our title from ‘ post.

Python Iterators and the Iterator protocol

Iterators allow you to iterate over any object that follows the specification.In the first section, we will see how to use iterators and make any object iterable.

The second part of this blog post focuses entirely on generators: what they are, how to use them, and in which situations they can be useful.I always like to look at how things work under the hood: In a previous blog series, I explained how JavaScript works in the browser.Before we can understand generators, we need a thorough understanding of iterators in JavaScript, as these two concepts go hand-in-hand.

After this section, it will become clear that generators are simply a way to write iterators more securely.As the name already gives away, iterators allow you to iterate over an object arrays are also objects.Most likely, you have already used JavaScript iterators.

Every time you iterated over an array, for example, you have used iterators, but you can also iterate over Map objects and even over strings.

Digging a bit deeper, you can make any object iterable by implementing the iterator function, which returns an iterator object.To make this object iterable, we first need to add the iterator function.We can access this symbol via Symbol.As I mentioned before, the iterator function returns an iterator object.The object contains a function under next , which also returns an object with two attributes: done and value.When implementing this function, we need to be especially careful about the done value, as it is always returning false will result in an infinite loop.

The code example above already represents a correct implementation of the iterable protocol.We can test it by calling the next function of the iterator object.First of all, we need to access the keys of the object that represent cities.We can get this by calling Object.We can only access the keys through this if we defined the iterable function with the function keyword.

We define these variables in the iterator function but outside of the next function, which allows us to keep the data between iterations.In the next function, we first need to get the array of users of the current city and the current user, using the indexes we defined before.We increment the user index every time unless we have arrived at the last user of a given city, in which case we will set userIndex to 0 and increment the city index instead.

Given that done always equals false , this will result in an infinite loop.The last thing we need to add is an exit condition that sets done to true.We exit the loop after we have iterated over all cities.As you can see, making an object iterable is not magic.However, it needs to be done very carefully because mistakes in the next function can easily lead to an infinite loop.If you want to learn more about the behavior, I encourage you to try to make an object of your choice iterable as well.

You can find an executable version of the code in this tutorial on this codepen.We need to be very careful when programming iterators, as bugs can have serious consequences, and managing the internal logic can be challenging.

An essential characteristic of generators and iterators is that they allow you to stop and continue execution as needed.We will see a few examples in this section that make use of this feature.Creating a generator function is very similar to regular functions.

If we want to create an anonymous generator function, this asterisk moves to the end of the function keyword.As mentioned, generators are an easier way to create iterables.But how does the iterator know over which part of the function it should iterate? Should it iterate over every single line?

That is where the yield keyword comes into play.You can think of it as the await keyword you may know from JavaScript Promises, but for generators.

We can add this keyword to every line where we want the iteration to stop.The done property is either true or false.The yield is a magical keyword that can do more things other than simply return a value and next can do more things aside from retrieving the value.A passing argument to next – The argument passed to next will be received by yield —.In this example; to fetch data from API, we have to install node-fetch using the command — ‘ npm install node-fetch’.We then pass a generator to a function as a parameter.

Let’s call the function getTitle.Initially, we will call the generator method.It returns an iterator object which is caught in a variable ‘ iterator ‘.When the next method is called, the generator starts executing from this point.At line 4 the ‘ URL ‘ is fetched.Fetch returns an object which is captured into variable ‘ iteration ‘.In our case, the getTitle function has to resolve the promise.

So for resolving that promise we caught iteration.We send the resolved ‘ x’ to the iterator’s next method.This x is a response which we collect into a variable ‘ response’.

The response object now again has a promise which is to be resolved by our function.So we extract the object into variable ‘ anotherIterator’ and the value of that object viz.

Now we resolve that promise in ‘ y’ and pass it to the generator through the next method.Here we used the second yield.So now we have resolved the response.Finally, we get our title through the object ‘ post’.Now we are going to extract our title from ‘ post.Check the console for the title.This is an evaluation model that delays the evaluation of an expression until its value is needed.That is, if the value is not needed, it will not exist.It is calculated on demand.The only values generated are those that are needed.

With normal functions, all the values must be pre-generated and kept around case they need to be used later.Our Design experiments and success stories.Design Open Source Illustration System.Stay tuned to our Design Podcast Designwise.Mayuri Papat.But its complexity grows when you nest a loop inside another loop.

So, you could use it to retrieve a function that iterates over an array object, like so — An iterator is an object that can access one item at a time from a collection while keeping track of its current position It just requires that you have a method called next to move to the next item to be a valid iterator The result of next is always an object with two properties — Value: The value in the iteration sequence Done: true false Generators Generator functions once called, returns the Generator object, which holds the entire Generator iterable and can be iterated using next method.

: Please do not confuse standards for duck typing with their implementation how a particular Python interpreter implemented it.

Jyo the Whiff Jyo the Whiff 8 8 silver badges 23 23 bronze badges.Iterators are better in some cases.Skip to content.

  • I hope you were able to understand how these two concepts work together and what they can be used for.
  • The for-of loop uses that iterable to retrieve the next yield as a [key, pair] value.
  • Besides, if you check the memory footprint, the generator takes much less memory as it doesn’t need to store all the values in memory at the same time.
  • The value property will contain the value.
  • I am confused.

The second part of this blog post focuses entirely on generators: what they are, how to use them, and in which situations they can be useful.I always like to look at how things work under the hood: In a previous blog series, I explained how JavaScript works in the browser.Before we can understand generators, we need a thorough understanding of iterators in JavaScript, as these two concepts go hand-in-hand.After this section, it will become clear that generators are simply a way to write iterators more securely.

As the name already gives away, iterators allow you to iterate over an object arrays are also objects.Most likely, you have already used JavaScript iterators.Every time you iterated over an array, for example, you have used iterators, but you can also iterate over Map objects and even over strings.

Digging a bit deeper, you can make any object iterable by implementing the iterator function, which returns an iterator object.To make this object iterable, we first need to add the iterator function.

We can access this symbol via Symbol.As I mentioned before, the iterator function returns an iterator object.The object contains a function under next , which also returns an object with two attributes: done and value.When implementing this function, we need to be especially careful about the done value, as it is always returning false will result in an infinite loop.

The code example above already represents a correct implementation of the iterable protocol.We can test it by calling the next function of the iterator object.First of all, we need to access the keys of the object that represent cities.We can get this by calling Object.We can only access the keys through this if we defined the iterable function with the function keyword.

We define these variables in the iterator function but outside of the next function, which allows us to keep the data between iterations.In the next function, we first need to get the array of users of the current city and the current user, using the indexes we defined before.

We increment the user index every time unless we have arrived at the last user of a given city, in which case we will set userIndex to 0 and increment the city index instead.Given that done always equals false , this will result in an infinite loop.The last thing we need to add is an exit condition that sets done to true.

We exit the loop after we have iterated over all cities.As you can see, making an object iterable is not magic.However, it needs to be done very carefully because mistakes in the next function can easily lead to an infinite loop.If you want to learn more about the behavior, I encourage you to try to make an object of your choice iterable as well.

You can find an executable version of the code in this tutorial on this codepen.We need to be very careful when programming iterators, as bugs can have serious consequences, and managing the internal logic can be challenging.An essential characteristic of generators and iterators is that they allow you to stop and continue execution as needed.

We will see a few examples in this section that make use of this feature.Creating a generator function is very similar to regular functions.If we want to create an anonymous generator function, this asterisk moves to the end of the function keyword.

As mentioned, generators are an easier way to create iterables.But how does the iterator know over which part of the function it should iterate? Should it iterate over every single line? That is where the yield keyword comes into play.You can think of it as the await keyword you may know from JavaScript Promises, but for generators.

We can add this keyword to every line where we want the iteration to stop.Once the function reaches its end, value equals undefined , and done is automatically set to true.If we add an asterisk to the yield keyword, we delegate the execution to another iterator object.When there is only one argument to the calling function, the parenthesis around generator expression can be omitted.

Lets say we want to find first 10 or any n pythogorian triplets.It is easy to solve this problem if we know till what value of z to test for.But we want to find first n pythogorian triplets.Lets say we want to write a program that takes a list of filenames as arguments and prints contents of all those files, like cat command in unix.

Now, lets say we want to print only the line which has a particular substring, like grep command in unix.Both these programs have lot of code in common.It is hard to move the common part to a function.But with generators makes it possible to do it.The code is much simpler now with each function doing one small thing.We can move all these functions into a separate module and reuse it in other programs.

Problem 2: Write a program that takes one or more filenames as arguments and prints all the lines which are longer than 40 characters.Problem 3: Write a function findfiles that recursively descends the directory tree for the specified directory and generates paths of all the files in the tree.Problem 4: Write a function to compute the number of python files.Problem 5: Write a function to compute the total number of lines of code in all python files in the specified directory recursively.

Problem 6: Write a function to compute the total number of lines of code, ignoring empty and comment lines, in all python files in the specified directory recursively.Problem 7: Write a program split.The itertools module in the standard library provides lot of intersting tools to work with iterators.Problem 8: Write a function peep , that takes an iterator as argument and returns the first element and an equivalant iterator.Problem 9: The built-in function enumerate takes an iteratable and returns an iterator over pairs index, value for each value in the source.

Problem Implement a function izip that works like itertools.Generator Tricks For System Programers by David Beazly is an excellent in-depth introduction to generators and generator expressions.

Python Practice Book.Getting Started 2.Working with Data 3.Modules 4.Object Oriented Programming 5.

Example Iterator Objects

I just wanted to give a short few lines answer for people who are still not quite clear conceptually:.If you create your own iterator, it is a little bit involved – you have to create a class and at least implement the iter and the next methods.

But what if you don’t want to go through this hassle and want to quickly create an iterator.Fortunately, Python provides a short-cut way to defining an iterator.

All you need to do is define a function with at least 1 call to yield and now when you call that function it will return ” something ” which will act like an iterator you can call next method and use it in a for loop.This something has a name in Python called Generator.Examples from Ned Batchelder highly recommended for iterators and generators.

A book full of pages is an iterable , A bookmark is an iterator.This abstraction makes it most usable in the large than simple iterators.

I tend to look at iterators as a low level primitive, except as literals.For control flow matters, generators are an as much important concept as promises: both are abstract and composable.

A Generator function is just like a regular function in Python but it contains one or more yield statements.Generator functions is a great tool to create Iterator objects as easy as possible.The Iterator object returend by generator function is also called Generator object or Generator.

Just like other iterators, Generator objects can be used in a for loop or with the built-in function next which returns the next value from generator.Every generator object is an iterator but not vice versa.However, it is much easier to use generators function to create iterators because they simplify their creation, but a custom Iterator gives you more freedom and you can also implement other methods according to your requirements as shown in the below example.

It’s difficult to answer the question without 2 other concepts: iterable and iterator protocol.What are iterables in python? What are iterators? We may check this using the approach above.

It’s kind of confusing.Probably it would be easier if we have only one type.Is there any difference between range and zip? One of the reasons to do this – range has a lot of additional functionality – we may index it or check if it contains some number etc.How can we create an iterator ourselves?

Theoretically we may implement Iterator Protocol see here.But in practice we use generators.It seems to be by far the main method to create iterators in python.I can give you a few more interesting examples that show somewhat confusing usage of those concepts in practice:.Besides, if you check the memory footprint, the generator takes much less memory as it doesn’t need to store all the values in memory at the same time.

I am writing specifically for Python newbies in a very simple way, though deep down Python does so many things.In python as soon as you introduce the keyword yield ; it becomes a generator function and iterator will be applied implicitly.Note: Every generator is always iterable with implicit iterator applied and here implicit iterator is the crux So the generator function will be:.

This thread covers in many details all the differences between the two, but wanted to add something on the conceptual difference between the two:.Sure, it does not cover all the aspects but I think it gives a good notion when one can be useful.

Stack Overflow for Teams — Collaborate and share knowledge with a private group.Create a free Team What is Teams? Collectives on Stack Overflow.Learn more.Asked 11 years, 8 months ago.Active 1 month ago.

Viewed k times.Add a comment.Active Oldest Votes.Alex Martelli Alex Martelli k gold badges silver badges bronze badges.Can you clarify what the correct lingo is here.I hear a lot of people using the term “Generator” interchangeably with “Generator Function” and “Generator Expression”, like in a Generator Function is a Generator and a Generator Expression is a Generator.

I am confused.A Generator is an Iterator Specifically, generator is a subtype of iterator.GeneratorType, collections.Iterator True We can create a generator several ways.Iterator, collections.Emphasis added.So from this we learn that Generators are a convenient type of Iterator.

Example Iterator Objects You might create object that implements the Iterator protocol by creating or extending your own object.Maggyero 4, 3 3 gold badges 26 26 silver badges 46 46 bronze badges.

Iterators: Iterator are objects which uses next method to get next value of sequence.Generators: A generator is a function that produces or yields a sequence of values using yield method.

Instead of f.The Python tutorial remarkably manages to imply both usages in the space of three sentences: Generators are a simple and powerful tool for creating iterators.Despite all this confusion, one can seek out the Python language reference for the clear and final word: The yield expression is only used when defining a generator function, and can only be used in the body of a function definition.

However, the Python 3 glossary states that generator Community Bot 1 1 1 silver badge.Paul Paul 3, 1 1 gold badge 28 28 silver badges 41 41 bronze badges.I don’t think there’s much confusion between generator functions and generator objects, for the same reason there’s usually no confusion between classes and their instances.In both cases, you call one to get the other, and in casual conversation or quickly written documentation you can use the class name or the word “generator” for either one.

You only need to be explicit about “generator function” versus “generator object” in rare situations where which one you’re talking about matters.Regardless of theoretical reasons for why there shouldn’t be confusion, comments on other answers to this question deny and contradict one another without resolution, indicating actual confusion exists.

Casual imprecision is fine but a precise, authoritative source should at least be one of the options on SO.I use both generator functions and objects extensively in my current project, and the distinction is very important when designing and coding.It’s good to know what terminology to use now, so I don’t have to change dozens of variable names and comments later on.Imagine a mathematics literature where no distinction is made between a function and its return value.

It is occasionally convenient to conflate them informally, but it increases the risk of a variety of mistakes.Advanced modern mathematics would be significantly and needlessly hampered if the distinction were not formalized in convention, language, and notation.Higher-order functions passing around generators or generator functions may sound weird, but for me they have been coming up.I’m working in Apache Spark and it enforces a very functional programming style.

The functions have to create, pass in, and pass out all sorts of objects to get things done.I’ve had a number of situations where I lost track of what kind of “generator” I was working with.Hints in variable names and comments, using the consistent and correct terminology, helped clear up the confusion.

One Pythonist’s obscurity can be the center of another’s project design! Paul, thanks for writing this answer.This confusion is important because the difference between a generator object and a generator function is the difference between getting the desired behavior and having to lookup generators.

Show 2 more comments.I just wanted to give a short few lines answer for people who are still not quite clear conceptually: If you create your own iterator, it is a little bit involved – you have to create a class and at least implement the iter and the next methods.

This something has a name in Python called Generator Hope that clarifies a bit.JavaScript provides several ways of iterating over a collection, from simple for loops to map and filter.Iterators and generators usually come as a secondary thought when writing code, but if you can take a few minutes to think about how to use them to simplify your code, they’ll save you from a lot of debugging and complexities.

Plain objects are not iterable and hence the ‘ for The Symbol.So, you could use it to retrieve a function that iterates over an array object, like so —.Generator functions once called, returns the Generator object, which holds the entire Generator iterable and can be iterated using next method.Every next call on the generator executes every line of code until it encounters the next yield and suspends its execution temporarily.

Generators are a special type of function in JavaScript that can pause and resume state.A Generator function returns an iterator, which can be used to stop the function in the middle, do something, and then resume it whenever.An async function can be decomposed into a generator and promise implementation which is good to know stuff.Additionally, generators can also receive input and send output via yield.

In short, a generator appears to be a function but it behaves like an iterator.A generator is a function that returns an object on which you can call next.

Every invocation of next will return an object of shape —.The value property will contain the value.The done property is either true or false.The yield is a magical keyword that can do more things other than simply return a value and next can do more things aside from retrieving the value.

A passing argument to next – The argument passed to next will be received by yield —.In this example; to fetch data from API, we have to install node-fetch using the command — ‘ npm install node-fetch’.

We then pass a generator to a function as a parameter.Let’s call the function getTitle.Initially, we will call the generator method.It returns an iterator object which is caught in a variable ‘ iterator ‘.When the next method is called, the generator starts executing from this point.At line 4 the ‘ URL ‘ is fetched.Fetch returns an object which is captured into variable ‘ iteration ‘.In our case, the getTitle function has to resolve the promise.

So for resolving that promise we caught iteration.We send the resolved ‘ x’ to the iterator’s next method.This x is a response which we collect into a variable ‘ response’.The response object now again has a promise which is to be resolved by our function.So we extract the object into variable ‘ anotherIterator’ and the value of that object viz.Now we resolve that promise in ‘ y’ and pass it to the generator through the next method.Here we used the second yield.

What are generators?.An iterator only needs to know the current position in the collection as opposed to other loops where they require to load the entire collection upfront in order to loop through it.

#61 Python Tutorial for Beginners – Iterator, time: 10:48
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