How do I turn a long one-line list, such as Show
into a one element per line list:
Or:
With
But I would find a one element per line list in many cases more useful. Is anyone aware of such a elisp function (e.g. in elpy or anywhere else)? Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Understanding Python List 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. 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. Some programmers even use them too much, which can lead to code that’s less efficient and harder to read. By the end of this tutorial, you’ll understand the full power of Python list comprehensions and how to use their features comfortably. You’ll also gain an understanding of the trade-offs that come with using them so that you can determine when other approaches are more preferable. In this tutorial, you’ll learn how to:
How to Create Lists in PythonThere are a few different ways you can create lists in Python. To better understand the trade-offs of using a list comprehension in Python, let’s first see how to create lists with these approaches. Using for LoopsThe most common type of loop is the
If you want to create a list containing the first ten perfect squares, then you can complete these steps in three lines of code: >>>
Here, you instantiate an empty list, Using map() Objects
As an example, consider a situation in which you need to calculate the price after tax for a list of transactions: >>>
Here, you have an iterable Using List ComprehensionsList comprehensions are a third way of making lists. With this elegant approach, you could rewrite the >>>
Rather than creating an empty list and adding each element to the end, you simply define the list and its contents at the same time by following this format: >>>
Every list comprehension in Python includes three elements:
Because the expression requirement is so flexible, a list
comprehension in Python works well in many places where you would use >>>
The only distinction between this implementation and Benefits of Using List ComprehensionsList comprehensions are often described as being more Pythonic than loops or One main benefit of using a list comprehension in Python is that it’s a single tool that you can use in many different situations. In addition to standard list creation, list comprehensions can also be used for mapping and filtering. You don’t have to use a different approach for each scenario. This is the main reason why
list comprehensions are considered Pythonic, as Python embraces simple, powerful tools that you can use in a wide variety of situations. As an added side benefit, whenever you use a list comprehension in Python, you won’t need to remember the proper order of arguments like you would when you call List comprehensions are also more declarative than loops, which means they’re easier to read and understand. Loops require you to focus on how the list is created. You have to manually create an empty list, loop over the elements, and add each of them to the end of the list. With a list comprehension in Python, you can instead focus on what you want to go in the list and trust that Python will take care of how the list construction takes place. How to Supercharge Your ComprehensionsIn order to understand the full value that list comprehensions can provide, it’s helpful to understand their range of possible functionality. You’ll also want to understand the changes that are coming to the list comprehension in Python 3.8. Using Conditional LogicEarlier, you saw this formula for how to create list comprehensions: >>>
While this formula is accurate, it’s also a bit incomplete. A more complete description of the comprehension formula adds support for optional conditionals. The most common way to add conditional logic to a list comprehension is to add a conditional to the end of the expression: >>>
Here, your conditional statement comes just before the closing bracket. Conditionals are important because they allow list comprehensions to filter out unwanted values, which would normally require a call to >>>
In this code block, the conditional statement filters out any characters in The conditional can test any valid expression. If you need a more complex filter, then you can even move the conditional logic to a separate function: >>>
Here, you create a complex filter You can place the conditional at the end of the statement for simple filtering, but what if you want to change a member value instead of filtering it out? In this case, it’s useful to place the conditional near the beginning of the expression: >>>
With this formula, you can use conditional logic to select from multiple possible output options. For example, if you have a list of prices, then you may want to replace negative prices with >>>
Here, your expression >>>
Now, your conditional statement is contained within Using Set and Dictionary ComprehensionsWhile the list comprehension in Python is a common tool, you can also create set and dictionary comprehensions. A set comprehension is almost exactly the same as a list comprehension in Python. The difference is that set comprehensions make sure the output contains no duplicates. You can create a set comprehension by using curly braces instead of brackets: >>>
Your set comprehension outputs all the unique
vowels it found in Dictionary comprehensions are similar, with the additional requirement of defining a key: >>>
To create the Using the Walrus OperatorPython 3.8 will introduce the assignment expression, also known as the walrus operator. To understand how you can use it, consider the following example. Say you need to make ten requests to an API that will return temperature data. You only want to return results that are greater than 100 degrees Fahrenheit. Assume that each request will return different data. In this case, there’s no way to use a list comprehension in Python to solve the problem. The formula The walrus operator solves this problem. It allows you to run an expression while simultaneously assigning the output value to a variable. The following example shows how this is possible, using >>>
You won’t often need to use the assignment expression inside of a list comprehension in Python, but it’s a useful tool to have at your disposal when necessary. When Not to Use a List Comprehension in PythonList comprehensions are useful and can help you write elegant code that’s easy to read and debug, but they’re not the right choice for all circumstances. They might make your code run more slowly or use more memory. If your code is less performant or harder to understand, then it’s probably better to choose an alternative. Watch Out for Nested ComprehensionsComprehensions can be nested to create combinations of lists, dictionaries, and sets within a collection. For example, say a climate laboratory is tracking the high temperature in five different cities for the first week of June. The perfect data structure for storing this data could be a Python list comprehension nested within a dictionary comprehension: >>>
You create the outer collection Nested lists are a common way to create matrices, which are often used for mathematical purposes. Take a look at the code block below: >>>
The outer list comprehension So far, the purpose of each nested comprehension is pretty intuitive. However, there are other situations, such as flattening nested lists, where the logic arguably makes your code more confusing. Take this example, which uses a nested list comprehension to flatten a matrix: >>>
The code to flatten the matrix is concise, but it may not be so intuitive to understand how it works. On the other hand, if
you were to use >>>
Now you can see that the code traverses one row of the matrix at a time, pulling out all the elements in that row before moving on to the next one. While the single-line nested list comprehension might seem more Pythonic, what’s most important is to write code that your team can easily understand and modify. When you choose your approach, you’ll have to make a judgment call based on whether you think the comprehension helps or hurts readability. Choose Generators for Large DatasetsA list comprehension in Python works by loading the entire output list into memory. For small or even medium-sized lists, this is generally fine. If you want to sum the squares of the first one-thousand integers, then a list comprehension will solve this problem admirably: >>>
But what if you wanted to sum the squares of the first billion integers? If you tried then on your machine, then you may notice that your computer becomes non-responsive. That’s because Python is trying to create a list with one billion integers, which consumes more memory than your computer would like. Your computer may not have the resources it needs to generate an enormous list and store it in memory. If you try to do it anyway, then your machine could slow down or even crash. When the size of a list becomes problematic, it’s often helpful to use a generator instead of a list comprehension in Python. A generator doesn’t create a single, large data structure in memory, but instead returns an iterable. Your code can ask for the next value from the iterable as many times as necessary or until you’ve reached the end of your sequence, while only storing a single value at a time. If you were to sum the first billion squares with a generator, then your program will likely run for a while, but it shouldn’t cause your computer to freeze. The example below uses a generator: >>>
You can tell this is a generator because the expression isn’t surrounded by brackets or curly braces. Optionally, generators can be surrounded by parentheses. The example above still requires a lot of work, but it performs the operations lazily. Because of lazy evaluation, values are only calculated when they’re explicitly requested. After the generator yields a value (for example,
>>>
It’s up to you whether you prefer the generator expression or
Profile to Optimize PerformanceSo, which approach is faster? Should you use list comprehensions or one of their alternatives? Rather than adhere to a single rule that’s true in all cases, it’s more useful to ask yourself whether or not performance matters in your specific circumstance. If not, then it’s usually best to choose whatever approach leads to the cleanest code! If you’re in a scenario where performance is important, then it’s typically best to profile different approaches and listen to the data. >>>
Here, you define three methods that each use a different approach for creating a list. Then, you tell As the code demonstrates, the biggest difference is between the loop-based approach and ConclusionIn this tutorial, you learned how to use a list comprehension in Python to accomplish complex tasks without making your code overly complicated. Now you can:
Whenever you have to choose a list creation method, try multiple implementations and consider what’s easiest to read and understand in your specific scenario. If performance is important, then you can use profiling tools to give you actionable data instead of relying on hunches or guesses about what works the best. Remember that while Python list comprehensions get a lot of attention, your intuition and ability to use data when it counts will help you write clean code that serves the task at hand. This, ultimately, is the key to making your code Pythonic! Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Understanding Python List Comprehensions How do I print a list on the same line?To print a list and display it in a single line, a straightforward solution is to iterate over each element in a for loop and print this element into the same line using the print() function with the end=' ' argument set to the empty space.
How do you return a list in Python?To return a list in Python, use the return keyword and write the list you want to return inside the function. Python list is like the array of elements created using the square brackets.
How do I print on the same line in Python?To print on the same line in Python, add a second argument, end=' ', to the print() function call.
How do I print an array in one line in Python?“print array in single line python” Code Answer's. >>> l = [1, 2, 3]. >>> print(' '. join(str(x) for x in l)). 1 2 3.. >>> print(' '. join(map(str, l))). 1 2 3.. |