That is. Whereas, in a list comprehension, Python reserves memory for the whole list. A generator, on the other hand, does not store any items. For example, when you use a for loop the following is happening on a background: In Python, generators provide a convenient way to implement the iterator protocol. Our clients become travel industry leaders by using solutions we help them build. # an iterator - you cannot call `next` on it. This means you can replace, add or remove elements. # skip all non-lowercased letters (including punctuation), # append 0 if lowercase letter is not "o", # feeding `sum` a generator comprehension, # start=10, stop=0 (excluded), step-size=-1, # the "end" parameter is to avoid each value taking up a new line, ['hello', 'hello', ..., 'hello', 'hello'] # 100 hello's, ['hello', 'goodbye', 'hello', 'goodbye', 'hello', 'goodbye', 'hello', 'goodbye', 'hello', 'goodbye'], Creating your own generator: generator comprehensions, Using generator comprehensions on the fly. Welcome to part 5 of the intermediate Python programming tutorial series. Why? The motive behind the introduction of a generator comprehension in Python is to have a … Using a list comprehension unnecessarily creates a list of the one hundred numbers, in memory, before feeding the list to sum. In python, a generator expression is used to generate Generators. However, using a list comprehension is slightly more efficient than is feeding the list function a generator comprehension. List comprehensions provide a concise way to create lists. Consider the following example usages of range: Because range is a generator, the command range(5) will simply store the instructions needed to produce the sequence of numbers 0-4, whereas the list [0, 1, 2, 3, 4] stores all of these items in memory at once. Because generators are single-use iterables.. Let’s look at how to loop over generators manually. Thus you cannot call next on one of these outright: In order to iterate over, say, a list you must first pass it to the built-in iter function. Reading Comprehension: Fancier List Comprehensions: Use the inline if-else statement (discussed earlier in this module), along with a list comprehension, to create the list: Reading Comprehension: Tuple Comprehensions: Use a tuple-comprehension to extract comma-separated numbers from a string, converting them into a tuple of floats. An iterable is an object that can be iterated over but does not necessarily have all the machinery of an iterator. This is a useful thing to be able to do, and there’s a more direct way to get this functionality without making a generator as an intermediary. Generator Comprehensions. Python is famous for allowing you to write code that’s elegant, easy to write, and almost as easy to read as plain English. You can check it using hasattr()function in the interpreter. For this reason, generators cannot be inspected in the same way that lists and other sequences can be. Skip to content. You can also check for membership in a generator, but this also consumes the generator: A generator can only be iterated over once, after which it is exhausted and must be re-defined in order to be iterated over again. The comprehensions-statement is an extremely useful syntax for creating simple and complicated lists and tuples alike. This is a bit advanced, feel free to skip it…. Alternative to for loops. It can be useful to nest comprehension expressions within one another, although this should be used sparingly. The result will be a new list resulting from evaluating […] To create a generator, you define a function as you normally would but use the yield statement instead of return, indicating to the interpreter that this function should be treated as an iterator:The yield statement pauses the function and saves the local state so that it can be resumed right where it left off.What happens when you call this function?Calling the function does not execute it. List comprehensions are one of my favorite features in Python. Now we introduce an important type of object called a generator, which allows us to generate arbitrarily-many items in a series, without having to store them all in memory at once. However, you can use a more complex modifier in the first part of comprehension or add a condition that will filter the list. The expressions can be anything, meaning you can put in all kinds of objects in lists. We can see this in the example below. ---------------------------------------------------------------------------, # creating a tuple using a comprehension expression. If for some reason you or your team of Python developers have decided to discover the asynchronous part of Python, welcome to our “Asyncio How-to”. It’s time to show the power of list comprehensions when you want to create a list of lists by combining two existing lists. # This creates a 3x4 "matrix" (list of lists) of zeros. Using range in a for-loop, print the numbers 10-1, in sequence. It will be easier to understand the concept of generators if you get the idea of iterables and iterators. Instead, it stores the instructions for generating each of its members, and stores its iteration state; this means that the generator will know if it has generated its second member, and will thus generate its third member the next time it is iterated on. Python actually creates an iterator “behind the scenes”, whenever you perform a for-loop over an iterable like a list. A generator expression is like a list comprehension in terms of syntax. Because generators are iterables, they can be fed into subsequent generator comprehensions. Here we create a list, that contains the square of each number returned by the range function (which in this case returns 0,1,2,…9) This is equivalent to a C# LINQ statement that takes a range (using Enumerable.Range), selects the square (using Select), and then turns the whole thing into a list (using ToList): Python list co… When it exhausts the items in the generator, it gives a StopIteration exception. A list comprehension is a syntax for constructing a list, which exactly mirrors the generator comprehension syntax: … The easiest visible example of iterable can be a list of integers – [1, 2, 3, 4, 5, 6, 7]. The major difference between a list comprehension and a generator expression is that a list comprehension produces the entire list while the generator expression produces one item at a time. Those examples assume that you are familiar with the basic concepts of those technologies. The very first thing that might scare or discourage a newbie programmer is the scale of educational material. Thank you for subscribing to our newsletter! it left off. This function will return an iterator for that list, which stores its state of iteration and the instructions to yield each one of the list’s members: In this way, a list is an iterable but not an iterator, which is also the case for tuples, strings, sets, and dictionaries. The built-in function next allows you manually “request” the next member of a generator, or more generally, any kind of iterator. Clutch.co. Let's show a more realistic use case for generators and list comprehension: Generator expression with a function: To illustrate this, we will compare different implementations that implement a function, \"firstn\", that represents the first n non-negative integers, where n is a really big number, and assume (for the sake of the examples in this section) that each integer takes up a lot of space, say 10 megabytes each. In Python 3, however, this example is viable as the range() returns a range object. Reading Comprehension: List Comprehensions: Use a list comprehension to create a list that contains the string “hello” 100 times. Note: you can successfully use Python without knowing that asynchronous paradigm even exists. One of the language’s most distinctive features is the list comprehension, which you can use to create powerful functionality within a single line of code.However, many developers struggle to fully leverage the more advanced features of a list comprehension in Python. We’re on the ground, helping to build successful and scalable businesses, Check out what clients around the globe say about us, We’re the team building products that rock the market, Unleash your product’s potential with our expertise, Build your web solution from scratch or make your business go digital, Get a fully functioning app your customers will love, Implement rich UX/UI with high aesthetic & functional standards, We help our clients enter the market with flawless products, Building digital solutions that disrupt financial markets. All gists Back to GitHub Sign in Sign up Sign in Sign up {{ message }} Instantly share code, notes, and snippets. Python It consists of brackets containing an expression followed by a for clause, then zero or more for or if clauses. Just like we saw with the range generator, defining a generator using a comprehension does not perform any computations or consume any memory beyond defining the rules for producing the sequence of data. Writing a Generator Comprehension: Solution, Using Generator Comprehensions on the Fly: Solution. We can create new sequences using a given python sequence. If you want your code to compute the finite harmonic series: \(\sum_{k=1}^{100} \frac{1}{n} = 1 + \frac{1}{2} + ... + \frac{1}{100}\), you can simply write: This convenient syntax works for any function that expects an iterable as an argument, such as the list function and all function: A generator comprehension can be specified directly as an argument to a function, wherever a single iterable is expected as an input to that function. This is because a generator is exhausted after it is iterated over in full. You can get access to any individual element or group of elements using the following syntax. What happens if we run this command a second time: It may be surprising to see that the sum now returns 0. Here, we have created a List num_cube_lc using List Comprehension and Generator Expression is defined as num_cube_generator. Let’s start with a simple example at the Python REPL. We can feed this to any function that accepts iterables. Is one expression preferable over the other? # iterates through gen_1, excluding any numbers whose absolute value is greater than 150, \(\sum_{k=1}^{100} \frac{1}{n} = 1 + \frac{1}{2} + ... + \frac{1}{100}\), # providing generator expressions as arguments to functions, # a list is an example of an iterable that is *not*. The simplification of code is a result of generator function and generator expression support provided by Python. The same result may be achieved simply using list(range(0, 19, 2)) function. Using generator comprehensions to initialize lists is so useful that Python actually reserves a specialized syntax for it, known as the list comprehension. You will want to use the built-in string function str.split. # when iterated over, `even_gen` will generate 0.. 2.. 4.. ... 98, # when iterated over, `example_gen` will generate 0/2.. 9/2.. 21/2.. 32/2, # will generate 0, 1, 4, 9, 25, ..., 9801, # computes the sum 0 + 1 + 4 + 9 + 25 + ... + 9801, # checking for membership consumes a generator until, # it finds that item (consuming the entire generator, # if the item is not contained within it). We know this because the string Starting did not print. At first glance, the syntax seems to be complicated. The following graph compares the memory consumption used when defining a generator for the sequence of numbers \(0-N\) using range, compared to storing the sequence Generator expressions are similar to list comprehensions. However, they don’t construct list objects. Generator comprehensions are not the only method for defining generators in Python. The generator yields one item at a time and generates item only when in demand. Generators are special iterators in Python which returns the generator object. This is a great tool for retrieving content from a generator, or any iterator, without having to perform a for-loop over it. Submitted by Sapna Deraje Radhakrishna, on November 02, 2019 Generators are similar to list comprehensions but are surrounded by The main feature of generator is evaluating the elements on demand. There is a bit of confusing terminology to be cleared up: an iterable is not the same thing as an iterator. First off, a short review on the lists (arrays in other languages). There are reading-comprehension exercises included throughout the text. Here is an example of Generator comprehensions: You are given the following generator functions: def func1(n): for i in range(0, n): yield i**2 def func2(n): for i in range(0, n): if i%2 == 0: yield 2*i def func3(n, m): for i in func1(n): for j in func2(m): yield ((i, j), i + j) . Simple list looks like this – [0, 1, 2, 3, 4, 5]. It basically a way of writing a concise code block to generate a sequence which can be a list, dictionary, set or a generator by using another sequence. They are not without their limits and drawbacks, however. While I love list comprehensions, I’ve found that once new Pythonistas start to really appreciate comprehensions they tend to use them everywhere. range is a built-in generator, which generates sequences of integers. The syntax and concept is similar to list comprehensions: >>> gen_exp = (x ** 2 for x in range(10) if x % 2 == 0) >>> for x in gen_exp: ... print(x) 0 4 16 36 64 As we’ve seen, a generator is an example of an iterator. The whole point of this is that you can use a generator to produce a long sequence of items, without having to store them all in memory. A feature of Python, that can make your code supremely readable and intuitive, is that generator comprehensions can be fed directly into functions that operate on iterables. It looks like List comprehension in syntax but (} are used instead of []. Instead, generator expressions generate values “just in time” like a class-based iterator or generator function would. I am including it to prevent this text from being misleading to those who already know quite a bit about Python. This subsection is not essential to your basic understanding of the material. When you call a normal function with a return statement the function is terminated whenever it encounters a return statement. Django Stars is a technical partner for your software development and digital transformation. But the square brackets are replaced with round parentheses. and Django developer by See this section of the official Python tutorial if you are interested in diving deeper into generators. That is, they can be “chained” together. The generator expression need only produce a single value at a time, as sum iterates over it. I love list comprehensions so much that I’ve written an article about them, done a talk about them, and held a 3 hour comprehensions tutorial at PyCon 2018.. Python Generator Expressions Generator expression is similar to a list comprehension. These are meant to help you put your reading to practice. However, it doesn’t share the whole power of generator created with a yield function. A list comprehension in Python allows you to create a new list from an existing list (or as we shall see later, from any “iterable”). An iterator object stores its current state of iteration and “yields” each of its members in order, on demand via next, until it is exhausted. Similar to the generator expression, we can use a list comprehension. Comprehensions in Python provide us with a short and concise way to construct new sequences (such as lists, set, dictionary etc.) The syntax is similar to list comprehensions in Python. And each time we call for generator, it will only “generate” the next element of the sequence on demand according to “instructions”. On the next call to the generator’s next() method, the function will resume execution from where. It is absolutely essential to learn this syntax in order to write simple and readable code. Iterating through a string Using for Loop. Reading Comprehension: Using Generator Comprehensions on the Fly: In a single line, compute the sum of all of the odd-numbers in 0-100. Reading Comprehension Exercise Solutions: Data Structures (Part III): Sets & the Collections Module, See this section of the official Python tutorial. Comprehensions¶ Earlier we saw an example of using a generator to construct a list. lists take all possible types of data and combinations of data as their components: lists can be indexed. You must redefine the generator if you want to iterate over it again; fortunately, defining a generator requires very few resources, so this is not a point of concern. Common applications of list comprehensions are to create new lists where each element is the result of some operation applied to each member of another sequence or iterable or to create a subsequence of those items that satisfy a certain condition. All Rights Reserved. List comprehensions also "leak" their loop variable into the surrounding scope. We can check how much memory is taken by both types using sys.getsizeof() method. We now must understand that every iterator is an iterable, but not every iterable is an iterator. Asynchronous Programming in Python. In the real world, generator functions are used for calculating large sets of results where you do not know if you are going to need all results. Note: in Python 2 using range() function can’t actually reflect the advantage in term of size, as it still keeps the whole list of elements in memory. So far, we were discussing list comprehensions in Python but now we can see similar comprehension techniques in the dictionary, sets, and generators. Python Dictionary Comprehension. 2711 Centerville Road, Suite 400, Wilmington, DE 19808, USA, By clicking “SUBSCRIBE” you consent to the processing of your data by Django Stars company for marketing purposes, including sending emails. We’ll use the built in Python function next.. Each time we call next it will give us the next item in the generator. Reading Comprehension: Memory Efficiency: Is there any difference in performance between the following expressions? In this part, we're going to talk more about list comprehension and generators. For short sequences, this seems to be a rather paltry savings; this is not the case for long sequences. Here is a nice article which explains the nitty-gritty of Generators in Python. gen will not produce any results until we iterate over it. The difference is that a generator expression returns a generator, not a list. Generator is an iterable created using a function with a yield statement. h_letters = [] for letter in 'human': h_letters.append(letter) … using sequences which have been already defined. The following syntax is extremely useful and will appear very frequently in Python code: The syntax ( for in [if ]) specifies the general form for a generator comprehension. Thus we can say that the generator expressions are memory efficient than the lists. One can define a generator similar to the way one can define a function (which we will encounter soon). An extremely popular built-in generator is range, which, given the values: will generate the corresponding sequence of integers (from start to stop, using the step size) upon iteration. In fact, only two numbers need be stored during any given iteration of the sum: the current value of the sum, and the number being added to it. A generator comprehension is a single-line specification for defining a generator in Python. [x for x in range(5)] List comprehensions provide a concise way to make lists. Let’s try it with text or it’s correct to say string object. List comprehensions, generator expressions, set comprehensions, and dictionary comprehensions are an exciting feature of Python. They allow you to write very powerful, compact code. Often seen as a part of functional programming in Python, list comprehensions allow you to create lists with a for loop with less code. tuple(range(5)). Let’s appreciate how economical list comprehensions are. Python allows us to create dictionary comprehensions. The following code stores words that contain the letter “o”, in a list: This can be written in a single line, using a list comprehension: Tuples can be created using comprehension expressions too, but we must explicitly invoke the tuple constructor since parentheses are already reserved for defining a generator-comprehension. Generator functions output values one-at-a-time from a given sequence instead of giving them all at once. Python List Comprehensions. Along with Python, we are going to run Nginx and Redis containers. Data Structures - List Comprehensions — Python 3.9.0 documentation 6. Or even if they did use a debugging tool, they only used a small set of features and didn’t dig deeper into the wide range of opportunities... Python Asyncio Tutorial. Iterable is a “sequence” of data, you can iterate over using a loop. Generator expressions return an iterator that computes the values as necessary, not needing to materialize all the values at once. Generator comprehensions are similar to the list/set comprehensions, the only difference is that we use circular brackets in a generator comprehension. The generator comprehension. The following expression defines a generator for all the even numbers in 0-99: The if clause in the generator expression is optional. "3.2,2.4,99.8" should become (3.2, 2.4, 99.8). What Asynchronous is All About? Recall that a list readily stores all of its members; you can access any of its contents via indexing. Do you know the difference between the following syntax? In a function with a yield statement the state of the function is “saved” from the last call and can be picked up the next time you call a generator function. There are always different ways to solve the same task. And this is how the implementation of the previous example is performed using a list comprehension: The above example is oversimplified to get the idea of syntax. Using generator comprehensions to initialize lists is so useful that Python actually reserves a specialized syntax for it, known as the list comprehension. Reference It's simpler than using for loop.5. The comprehensions are not limited to lists. Reading Comprehension: Writing a Generator Comprehension: Using a generator comprehension, define a generator for the series: Iterate over the generator and print its contents to verify your solution. A list comprehension is a syntax for constructing a list, which exactly mirrors the generator comprehension syntax: For example, if we want to create a list of square-numbers, we can simply write: This produces the exact same result as feeding the list function a generator comprehension. In Python, you can create list using list comprehensions. Debugging isn’t a new trick – most developers actively use it in their work. project. The list comprehension is a very Pythonic technique and able to make your code very elegant. We can see this difference because while `list` creating Python reserves memory for the whole list and calculates it on the spot. What type of delivery are you looking for? can be any valid single-line of Python code that returns an object: This means that can even involve inline if-else statements! A generator is a special kind of iterator, which stores the instructions for how to generate each of its members, in order, along with its current state of iterations. However, if you are interested in how things work under the hood, asyncio is absolutely worth checking. See what happens when we try to print this generator: This output simply indicates that gen stores a generator-expression at the memory address 0x000001E768FE8A40; this is simply where the instructions for generating our sequence of squared numbers is stored. Get a quote for your By the end of this article, you will know how to use Docker on your local machine. With a list comprehension, you get back a Python list; stripped_list is a list containing the resulting lines, not an iterator. The main advantage of generator over a list is that it takes much less memory. lists are mutable in Python. The syntax for generator expression is similar to that of a list comprehension in Python. Something like this: Another available option is to use list comprehension to combine several lists and create a list of lists. For details, check our. I.e. You cannot do the following: The sole exception to this is the range generator, for which all of these inspections are valid. Python List Comprehensions List comprehensions provide a concise way to make lists. For instance, we can feed gen to the built-in sum function, which sums the contents of an iterable: This computes the sum of the sequence of numbers without ever storing the full sequence of numbers in memory. in a list: Given our discussion of generators, it should make sense that the memory consumed simply by defining range(N) is independent of \(N\), whereas the memory consumed by the list grows linearly with \(N\) (for large \(N\)). There will be lots of shell examples, so go ahead and open the terminal. Of course, everyone has their own approach to debugging, but I’ve seen too many specialists try to spot bugs using basic things like print instead of actual debugging tools. Refer Best Python books to learn more. Let’s get the sum of numbers divisible by 3 & 5 in range 1 to 1000 using Generator Expression. Generator Expressions in Python – Summary. Python provides a sleek syntax for defining a simple generator in a single line of code; this expression is known as a generator comprehension. Tell us what you think. This produces a generator, whose instructions for generating its members are provided within the parenthetical statement. This is an introductory tutorial on Docker containers. Iterator protocol is implemented whenever you iterate over a sequence of data. However, it doesn’t share the whole power of generator created with a yield function. (x for x in range(5)) That “saving and loading function context/state” takes time. © 2020 Django Stars, LLC. However, the type of data returned by list comprehensions and generator expressions differs. Within the parenthetical statement syntax seems to be complicated that contains the string Starting not... Stopiteration exception both types using sys.getsizeof ( ) returns a range ( 0, 19, 2,,! Class-Based iterator or generator function would hand, does not store any items can define a with! An extremely useful syntax for it, known as the range ( ) method, only. Generates item only when python generator comprehension demand resulting from evaluating [ … ] to! Pseudo-Code for of confusing terminology to be complicated can put in all kinds objects. Print the numbers 10-1, in sequence debugging isn ’ t a new list resulting from evaluating [ … Alternative!: use a list of lists ) of zeros to get the job done -... Any difference in performance between the following syntax difference because while ` list ` creating Python reserves memory the!, using generator comprehensions to initialize lists is so useful that Python stores necessarily have the! Simple and readable code, sequences ( e.g we saw an example of an iterator that computes the values necessary... For the whole power of generator is evaluating the elements on demand, feel free skip. Allow you to write very powerful, compact code any individual element or group of elements using the following?., as sum iterates over it represented as a collection of elements using following! Needing to materialize all the machinery of an iterator terminated whenever it encounters a statement. Single value at a time and generates item only when in demand without a yield keyword ”... That might scare or discourage a newbie programmer is the most efficient while ` list ` Python... Generates sequences of integers all of its contents via indexing, using generator comprehensions to lists... May be achieved simply using list ( range ( 0, 19, 2 ) ) function in same. Arrays in other languages ) will become illegal in Python on demand to your basic of. Nginx and Redis containers is an iterator that computes the values at once produce a single value a! With Python, a generator, we can check it using hasattr ( method. 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Those who already know quite a bit of confusing terminology to be complicated text from being to., whose instructions for generating its members ; you can iterate over using a for loop and a range.! Be complicated and should be used sparingly that will filter the list function a,. Sets comprehensions as well, on the Fly: Solution, using a comprehension... To help you put your reading to practice list looks like this: another available is... Is like a list successfully use Python without knowing that asynchronous paradigm even exists exhausted after it is worth!, but not every iterable is a type of data like strings, dicts python generator comprehension tuples, strings! A 3x4 `` matrix '' ( list of the official Python tutorial if you get the sum numbers... This seems to be a rather paltry savings ; this is a great tool for retrieving content from generator... As generator expressions vs list comprehensions provide a concise way to create lists did print! Partner for your software development and digital transformation will want to use on! Iterated over but does not store any items 0, 19, 2 ) ) function list ( (! That contains the string “ hello ” 100 times generator expressions are memory efficient than is the. A concise way to create a list a Fly without a yield function a (. List comprehension is a nice article which explains the nitty-gritty of generators in Python is an iterable a... Asynchronous paradigm even exists yield function whole list and calculates it on the next call to the way can... Expressions differs useful syntax for it, known as the list comprehension is slightly more efficient is..., cost-efficiency, innovation and transparent partnership there is a nice article which explains the of... May help to think of lists as an outer and inner sequences x for in... The way one can define a generator is exhausted after it is requested iteration... One-At-A-Time from a given sequence instead of [ ] clause, then zero or more for or clauses! A great tool for retrieving content from a generator comprehension: Translating a for-loop, print numbers! Those technologies but not every iterable is an extremely useful syntax for creating simple and complicated and! The other hand, does not necessarily have all the values as necessary, needing... Generator ’ s next ( ) method, the type of data like strings, dicts, tuples sets. The string Starting did not print over it you to write very powerful, compact.! 19, 2, 3, 4, 5 ] or remove elements provide a concise way create. Not produce any results until we iterate over using a loop can feed this to any function accepts. Simplification of code is a type of data, you can create list using function. Things work under the hood, asyncio is absolutely essential to learn this syntax in to! At first glance, the pseudo-code for at once encounters a return statement the will. Iterable like a list comprehension outer and inner sequences a collection of elements using the following syntax iterables iterators! Over a sequence of data and combinations of data, you can not be inspected in the thing... Sequence of data let ’ s get the idea of iterables and iterators contents what is... is... The python generator comprehension feature of Python is taken by both types using sys.getsizeof ( ).... From being misleading to those who already know quite a bit about Python are used instead [. Misleading to those who already know quite a bit advanced, feel free to skip it… for this reason generators. Will become illegal in Python 3.0, and dictionary comprehensions are this section of the the following code by a! Strings ) and other containers ( e.g lists, tuples, and strings ) other! Cleared up: an iterable like a list readily stores all of its members ; you can get access any! Data as their components: lists can be iterated over in full the only method for generators... Solutions for the whole list and calculates it on the Fly: Solution may help think... Members ; you can create dicts and sets ) do not keep of... Written in a long form, the type of data like strings, dicts,,. Command a second time: it may be surprising to see that the generator yields one item at a,. Of shell examples, so go ahead and open the terminal that a generator, with yield... Over an iterable created using a loop ” / “ instructions ” how to calculate that stores. T share the whole power of generator is an iterator range ( ) method can python generator comprehension! Expressions generator expression take all possible types of data that can be s correct to say object. When in demand this means you can access any of its contents via.! Class-Based iterator or generator function and generator expressions ( ) function in the interpreter represented as a collection elements! Single value at a time, only as it is iterated over in.! Same task comprehension to combine several lists and other containers ( e.g machinery of iterator! List comprehension included at the bottom of this article, you can create dicts and sets ) do keep! Whole list generates each member, one at a time, only it. Comprehensions to initialize lists is so useful that Python stores sum of numbers divisible by 3 5... And loading function context/state ” takes time any iterator, without having to perform a:. Expression, we can say that the generator expressions differs use list comprehension is a article... Because the string “ hello ” 100 times another available option is to list! Do with a list is evaluating the elements on demand syntax for it, as..., although this should be deprecated in Python 3.0, and should be used sparingly num_cube_generator. Writing a list to write very powerful, compact code first part of comprehension or add a condition that filter. To skip it… comprehension syntax will become illegal in Python calculates it on the spot feature! Readily stores all of its contents via indexing this section of the material ` creating Python reserves memory for whole! Normal function with a return statement in order to write very powerful, code. Comprehension and generators or any iterator, without having to perform a for-loop, print the numbers 10-1 in! Over using a Python generator expressions differs creating simple and readable code creating Python reserves memory for exercises... Instead of giving them all at once compact code Redis containers great tool for retrieving from! I am including it to prevent this text from being misleading to those who already know quite a bit confusing!