Guide to: Learning Iteration and Generators in Python

Type this stuff at the python prompt and see what happens:. So that was pretty simple. This is where iterators come in.

Difference between Python's Generators and Iterators - Stack Overflow

That sounds pretty fancy but don't be intimidated. Soon you will be able to create your own iterators. The one we are interested in is called next. Which is almost what we want. It has reached the end of the list and can't go any further.

Just what we wanted. Play with it a little, see what happens if you pass in the iterables from the introductory examples of this tutorial. And finally, the cool stuff. We will now make our own objects iterable. I'm going to detail the approach I usually use. I use it because it is simple and concise. I use the same class for both the iterator and the iterable. This is useful if you only need one iterator for your class which is most of the time. This approach of doing things has a few pros that I have already mentioned: But there are instances where you might want two iterators over the same data.

If, following from above, we again run the code:.

Nothing will be printed. We would have to reinitialise the class by setting o. While this is a definite weakness the solution is trivial and left as an exercise. Well done for getting this far. Knowing the ins and outs of for loops and iterators makes a lot of things much easier in Python.

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Now you should be in a position that you really get the for loop and its internal mechanisms. You can write for loops for iterating over many kinds of Python's build in iterables and, if that's not enough, you can even make your own iterables. If your code is very loopy and deals with a lot of collections then these skills will be indespensable and I would suggest continuing your education by learning about Python generators, list comprehension and sorting mechanisms.

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Also the itertools module should be of interest. On the other hand, if your code tends to involve tree or graph type structures and algorithms I would suggest looking into recursion. An elegant recursive function can replace many lines of nested loops.

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I need to get knowledge about for loop this was the formation that was use in my field of study so I need to get grip on it. I was little bit confuse between this so I took help from https: Another one which is good for beginners is python for loop tutorial. Find a mentor Web Programming. Awesome lists, learning plans, and reading lists for developers.

Python expert with a focus on web technologies, microservices and devops. I also do some frontend work React and Angular experience. Published Mar 02, Last updated Apr 12, Introduction and the Anatomy of a For Loop So we'll start off with some basics so everyone is on the same page. The basic anatomy of a for loop is a little something like: If you don't know how functions work yet, it'll be worth looking that up before continuing here y is an iterable.

Example Iterator Objects

We'll get into the details of what that means in a little bit. This can be seen in the output below:. Generators have a powerful tool in the send method for generator-iterators. This method was defined in PEP , and is available since Python version 2. The send method resumes the generator and sends a value that will be used to continue with the next yield.

The method returns the new value yielded by the generator. The syntax is send or send value. Without any value, the send method is equivalent to a next call. This method can also use None as a value. In both cases, the result will be that the generator advances its execution to the first yield expression. If the generator exits without yielding a new value like by using return , the send method raises StopIteration.

The following example illustrates the use of send. In the first and third lines of our generator, we ask the program to assign the variable number the value previously yielded. In the first line after our generator function, we instantiate the generator, and we generate a first yield in the next line by calling the next function.

Thus, in the last line we send the value 5, which will be used as input by the generator, and considered as its previous yield. Because there is no yielded value when the generator is first created, before using send , we must make sure that the generator yielded a value using next or send None. In the example above, we execute the next g line for just this reason, otherwise we'd get an error saying "TypeError: The third line of our generator from above also shows a new Python feature introduced in the same PEP: This feature allows the yield clause to be used on the right side of an assignment statement.

The value of a yield expression is None , until the program calls the method send value. The code above defines three different generators. The first, named myGenerator1 , has an input parameter, which is used to specify the limit in a range. The second, named myGenerator2 , is similar to the previous one, but contains two input parameters, which specify the two limits allowed in the range of numbers. After this, myGenerator3 calls myGenerator1 and myGenerator2 to yield their values.

The last three lines of code print on the screen three lists generated from each of the three generators previously defined. As we can see when we run the program below, the result is that myGenerator3 uses the yields obtained from myGenerator1 and myGenerator2 , in order to generate a list that combines the previous three lists. The example also shows an important application of generators: As you can see, thanks to the yield from syntax, generators can be chained together for more dynamic programming. As seen in the examples shown in this article, generators simplify code in a very elegant manner.

How — and why — you should use Python Generators

These code simplification and elegance are even more evident in generator expressions, where a single line of code replaces an entire block of code. Generators work on lazy on-demand generation of values. This results in two advantages. First, lower memory consumption. However, this memory saving will work in our benefit if we use the generator only once.

If we use the values several times, it may be worthwhile to generate them at once and keep them for later use. The on-demand nature of generators also means we may not have to generate values that won't be used, and thus would have been wasted cycles if they were generated. This means your program can use only the values needed without having to wait until all of them have been generated. Generators are an advanced tool present in Python. There are several programming cases where generators can increase efficiency. Some of these cases are:.

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Generators are a type of function that generate a sequence of values. As such they can act in a similar manner to iterators. Their use results in a more elegant code and improved performance. These aspects are even more evident in generator expressions, where one line of code can summarize a sequence of statements. Generators' working capacity has been improved with new methods, such as send , and enhanced statements, such as yield from. As a result of these properties, generators have many useful applications, such as generating pipes, concurrent programming, and helping in creating streams from large amounts of data.

As a consequence of these improvements, Python is becoming more and more the language of choice in data science. Subscribe to our newsletter! Get occassional tutorials, guides, and reviews in your inbox. Toggle navigation Stack Abuse. What is a Generator?

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The state of the function is maintained through the use of the keyword yield , which has the following syntax: How do Python Generators Work? In order to understand how generators work, let's use the simple example below: Calling the function two more times, provides us with the next 2 numbers in the sequence, as seen below: The Difference Between return and yield The keyword return returns a value from a function, at which time the function then loses its local state. Using return in a Generator A generator can use a return statement, but only without a return value, that is in the form: As the PEP states: The code is as follows: This can be seen below: Using next to Iterate through a Generator We can parse the values yielded by a generator using the next method, as seen in the first example.

For example, the following code will print on the screen the values 0 to 9. Running the code above will produce the following output: In Python 2, the next can use the previous syntax or the following syntax: For example, our code from before could be modified using generator expressions as follows: For example, the following code will sum the first 10 numbers: