![]() Rivas on Unsplash Create a list of items from iterable To help you visualize each part of the list comprehension, I’m keeping the words that separate them always in bold. Let’s see some examples that will make it easier to grasp this structure. However, the other three parts are always necessary for a list comprehension to work. You can use this or just skip it if you don’t need it. Condition makes use of if statements to filter your list items according to a condition.Lists, strings, sets, tuples, dictionaries, generators, and others are all iterable objects. Iterable is any Python object that is capable of returning each item one at a time.Item is each singular object or value in the iterable.Expression can be simply the item itself, a mathematical operation, a string method, a function, or even include a conditional statement or be another list comprehension, applied to the items.Here is the basic structure of a list comprehension: new_list = If you understand the structure of list comprehensions you will be able to apply it to most tasks by simply following its logic. They are usually more pythonic than for loops or map() because they require less lines of code and are so flexible that they can be applied to a wide range of situations. List comprehensions provide a concise way to create lists, and are specially useful when you are creating a list by applying some operation to each item from an iterable, and/or by filtering elements according to some condition. If you’d like to learn more about lists, I recommend this very clear and useful article on lists and also the official Python documentation. Isn’t that handy? This article, though, will not focus on all the great things you can do with lists in Python, but rather on a method to create them - list comprehensions. They can even contain functions, classes and modules, and each object can be accessed by its index. For example: my_list =, , True, (1,2,3)] type(my_list) list type(my_list) dict type(my_list) tupleĪs you can see, list objects can be of any type - integers or floats, strings, other lists, dictionaries, booleans, tuples. They are defined as ordered collections of arbitrary objects that are mutable, dynamic and that can be accessed by index. Lists are a very resourceful type of data structure in Python. ![]() Let’s start by reviewing a basic definition of lists. In this article you will learn the basics as well as some more advanced implementations of list comprehensions in Python and you will be ready to put it to good use. List comprehension is a very utilitarian feature in Python programming that can make your code not only faster but also easier to write and read.
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