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In Python, list comprehensions help you create new lists from existing iterables, like lists, strings, and tuples.

Their succinct syntax lets you create new lists in just one line of code. And this tutorial will teach you how you can do that.

Over the next few minutes, you’ll learn:

  • How to create a new list using for loops,
  • The syntax for using list comprehensions in Python, and
  • How to modify list comprehensions with if conditional statement.

In addition, you’ll also code several examples that will help you understand list comprehensions better.

Let’s dive in.🌊

How to Create Python Lists Using for Loops

Suppose you have a list number of numbers nums. And you’d like to create another list that contains the cube of all the numbers in nums. Here’s how you’ll do it using a for loop in Python:

nums = [2,3,5,7]
num_cubes = []
for num in nums:


# Output
[8, 27, 125, 343]

In the above code, we have the following steps:

  • Initialize an empty list num_cubes.
  • Loop through the nums list.
  • Access each number num, and compute its cube using the exponentiation operator: num**3.
  • Finally, append the cubed value to the list num_cubes

Note: In Python, the exponentiation operator ** is used with the syntax: num**pow—the number num is raised to the power pow.

However, you can do this more easily using list comprehension in Python. Let’s proceed to learn its syntax.

Python List Comprehension Syntax

The general syntax for list comprehension is shown below.

<new_list> = [<expression> for <item> in <iterable>]

Let’s parse the above syntax.

  • In Python, lists are delimited by a pair of square brackets []—hence you need to enclose the list comprehension statement within [].
  • <item> in <iterable> signifies that you’re looping through an iterable. Any Python object that you can loop through and access individual items—such as lists, tuples, and strings are iterables.
  • <expression> is the output that you’d like to compute for every <item> in the <iterable>.

And this sounds simple, yes?

In essence, you’d like to do something for all items in the list (or any iterable) to get a new list.

Using this, we can simplify the syntax, as shown in the image below.

Now that you’ve learned the syntax, it’s time to start coding. You can use Geekflare’s online Python IDE to follow along with these examples. Or you could run them on your local machine.

Python List Comprehension Examples

In the previous section, you created a new list num_cubes from nums. Let’s start by rewriting that using list comprehension.

Using List Comprehension with Numbers

Now let’s use the simplified syntax as follows:

  • <do-this>: Here, you have to cube each num. So replace <do-this> with num**3.
  • <all-items>: The looping variable is num—the individual numbers in the list.
  • <this-list>: The existing list we have is nums.
  • And [num**3 for num in nums] is the final expression. ✅

Putting it all together, we have the following code snippet:

num_cubes = [num**3 for num in nums]

# Output
[8, 27, 125, 343]

Congratulations, you’ve coded your first list comprehension.🎉

Moving on, let’s work with Python strings.

Using List Comprehension with Strings

Suppose you have the list authors —you can rewrite the list below with your favorite authors.😄

authors = ["jane austen","george orwell","james clear","cal newport"]

Notice how the authors’ names are in lowercase in the above list. We would now like to format them in the title case and store them in a new list called author_list.

Note: In Python, the string method title() accepts a string as an argument, and returns a copy of the string formatted in the title case. That is, the first letter of each word is capitalized: First-name Last-name

So here’s all you need to do:

  • loop through the authors list and for each author in the list,
  • call author.title() to get a title-cased copy of the string.

And the Python code for this is shown below:

authors = ["jane austen","george orwell","james clear","cal newport"]

author_list = [author.title() for author in authors]

# Output
['Jane Austen', 'George Orwell', 'James Clear', 'Cal Newport']

In the above output, observe how the names of all the authors have been formatted in the title case—which is what we wanted.

Using List Comprehension with Multiple Lists

So far, you’ve learned how to use list comprehension to create new lists from one existing list. Now let’s learn how to create a new list from multiple lists.

For example, consider this problem: You have two lists l_arr and b_arr containing the lengths and breadths of 4 rectangles.

And you need to create a new list area that includes the area of these 4 rectangles. Remember, area = length * breadth.

l_arr = [4,5,1,3]
b_arr = [2,1,7,9]

You’ll need elements from both the lists (l_arr and b_arr) in order to calculate the area. And you can do it using Python’s zip() function.

Note: In Python, the zip() function takes in one or more iterables as arguments with the syntax zip(*iterables). It then returns an iterator of tuples, where the tuple i contains the element i from each of the iterables.

The following image describes this in detail. You have 4 values in l_arr and b_arr, so the range of indices is from 0 to 3. As you can see, the tuple 0 contains l_arr[0] and b_arr[0], tuple 1 contains l_arr[1] and b_arr[1], and so on.

Therefore, you can loop through zip(l_arr,b_arr) as shown below:

area = [l*b for l,b in zip(l_arr,b_arr)]

# Output

In the next section, you’ll learn how to use conditional statements inside a list comprehension.

Python List Comprehension with Condition Syntax

Let’s start by building on the previous syntax for list comprehension.

Here’s the syntax:

<new_list> = [<expression> for <item> in <iterable> if <condition>]

Instead of computing the <expression> for all items, you’d only like to do it for those items that satisfy a specific <condition>—where, condition := True. And this leads to a simplified syntax as shown:

▶ With that, let’s proceed to code examples.

Python List Comprehension with Condition Examples

#1. You’re given the string “I’m learning Python in 2022”. You’d like to get a list of all digits in this string. So how do you do it?

In Python, <char>.isdigit() acts on a character <char> and returns True if it’s a digit (0-9); else it returns False.

The code snippet below shows how you can collect the list of all digits in the string str1.

str1 = "I'm learning Python3 in 2022"

digits = [char for char in str1 if char.isdigit()]


# Output
['3', '2', '0', '2', '2']

In the above code:

  • you loop through the string str1,
  • access each char to check if it’s a digit using the isdigit() method, and
  • add char to the new list digits only if it is a digit.

Let’s take another example.

#2. You have a list of fruits.🍊 And you’d like to create a list starts_with_b that contains all fruits from the fruits list that start with b. You can use the startswith() method to write the condition.

The <str>.startswith('char') returns True if <str> starts with ‘char’; else it returns False.

fruits = ['blueberry','apple','banana','orange','cherry']

starts_with_b = [fruit for fruit in fruits if fruit.startswith('b')]


# Output
['blueberry', 'banana']

In the output above, we get 'blueberry' and 'banana' which are the two fruits that start with 'b' in the fruits list, as we expected.

And that wraps up our discussion on list comprehension.


I hope this tutorial helped you understand list comprehensions in Python.

Let’s summarize:

  • You can use [<do this> for <all-items> in <this-list>] to create a new list using list comprehension.
  • Additionally, you can use the syntax [<do this> for <all-items> in <this-list> if <condition-is-True>] with the if conditional statement.

Additionally, you’ve also coded several examples. As a next step, you can try rewriting some of your existing Python loops for list creation using list comprehension. Happy coding! Until the next tutorial.😄

You may now look at how to convert a list to a dictionary or learn how to handle files in Python.

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  • Bala Priya C
    Bala Priya is a developer and technical writer from India with over three years of experience in the technical content writing space. She shares her learning with the developer community by authoring tech tutorials, how-to guides, and more….

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