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Effortlessly Master List Comprehension with If-Else


When to Use a List Comprehension in Python

One of Python’s most distinctive features is the list comprehension, which allows you to create powerful functionality within a single line of code. However, many developers struggle to fully leverage the more advanced features of list comprehensions in Python. Some programmers even use them too much, which can lead to less efficient and harder-to-read code.

In this tutorial, we will explore the full power of Python list comprehensions and learn how to use their features comfortably. We will also discuss the trade-offs that come with using them and determine when other approaches may be more suitable.

Transforming Lists in Python

There are several ways to create and add items to lists in Python. In this section, we will explore for loops and the map() function before diving into list comprehensions.

Use for Loops

The most common type of loop in Python is the for loop. You can use a for loop to create a list of elements by following these steps:

  1. Instantiate an empty list.
  2. Iterate over an iterable or range of elements.
  3. Append each element to the end of the list.

For example, if you want to create a list containing the first ten perfect squares, you can do so using a for loop:

squares = []
for number in range(10):
squares.append(number * number)

In this code, an empty list called squares is created. Then, a for loop iterates over the range from 0 to 9. Each number in the range is squared and appended to the squares list. The resulting list contains the first ten perfect squares: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81].

Work With map Objects

For a different approach rooted in functional programming, you can use the map() function. This function takes a function and an iterable as arguments and creates an object that contains the result of applying the function to each element in the iterable. To obtain a list from a map() object, you can use the list() function to convert it.

For example, if you want to calculate the squares of the first ten numbers using the map() function, you can do so as follows:

squares = list(map(lambda x: x * x, range(10)))

In this code, the lambda function calculates the square of each number in the range, and the map() function applies this function to each element in the range. Finally, the list() function is used to convert the map object into a list.

Leverage List Comprehensions

Now that we understand how to transform lists using for loops and the map() function, let’s explore list comprehensions. A list comprehension provides a more concise and expressive way to create lists in Python.

A basic list comprehension has the following syntax:

new_list = [expression for item in iterable]

Here, expression represents the operation or transformation to be applied to each item in the iterable. The resulting values are collected and stored in the new_list.

For example, let’s rewrite the previous example using a list comprehension:

squares = [number * number for number in range(10)]

In this code, the list comprehension creates a new list called squares by squaring each number in the range from 0 to 9. The resulting list is [0, 1, 4, 9, 16, 25, 36, 49, 64, 81].

List comprehensions can also include conditional logic to filter elements based on certain criteria. The syntax for incorporating an if statement into a list comprehension is as follows:

new_list = [expression for item in iterable if condition]

Here, condition represents the filtering criteria. Only the items that satisfy the condition will be included in the resulting list.

For example, let’s create a list of even numbers in the range from 0 to 9 using a list comprehension:

even_numbers = [number for number in range(10) if number % 2 == 0]

In this code, the list comprehension checks if each number in the range is divisible by 2 without a remainder (number % 2 == 0). Only the even numbers satisfy this condition and are included in the even_numbers list: [0, 2, 4, 6, 8].

By using list comprehensions, you can perform complex operations and filtering on lists in a concise and readable manner. They provide a flexible and efficient way to manipulate data in Python.

Supercharging Your Python List Comprehensions

List comprehensions can be further enhanced with additional features and techniques. Let’s explore some of these advanced concepts.

Filter Values From a List

In addition to using conditional statements to filter elements, list comprehensions can also filter specific values from an existing list. This can be done by applying an expression to exclude certain elements.

For example, suppose we have a list of numbers and we want to create a new list that excludes any numbers less than 5. We can achieve this with a list comprehension as follows:

original_list = [1, 3, 5, 7, 9, 2, 4, 6, 8]
filtered_list = [number for number in original_list if number >= 5]

In this code, the list comprehension checks if each number in the original_list is greater than or equal to 5 (number >= 5). Only the numbers that pass this condition are included in the filtered_list: [5, 7, 9, 6, 8].

By leveraging filtering capabilities, you can selectively modify or create new lists based on specific criteria.

Remove Duplicates With Set and Dictionary Comprehensions

List comprehensions can also be used to remove duplicates from a list. One approach is to convert the list to a set, which automatically removes duplicate elements, and then convert it back to a list.

For example, let’s remove duplicate numbers from a list using a set comprehension:

numbers = [1, 2, 3, 2, 4, 5, 3, 6]
unique_numbers = list({number for number in numbers})

In this code, the set comprehension {number for number in numbers} creates a set containing only the unique numbers from the numbers list. This set is then converted back to a list using the list() function.

Alternatively, you can use a dictionary comprehension to achieve the same result:

unique_numbers = list({number: None for number in numbers})

In this code, the dictionary comprehension {number: None for number in numbers} creates a dictionary where each number is a key and its value is set to None. When converting this dictionary to a list using list(), only the unique keys (numbers) are included.

Both approaches yield the same result: [1, 2, 3, 4, 5, 6]. By utilizing set or dictionary comprehensions, you can easily remove duplicates from a list while preserving the original order.

Assign Values With the Walrus Operator

Python 3.8 introduced the walrus operator (:=), which allows variable assignment within expressions. This operator can be useful when working with complex conditions or calculations inside a list comprehension.

For example, let’s create a list of consecutive odd numbers greater than 10 using the walrus operator:

odd_numbers = [number for number in range(20) if (value := number * 2 + 1) > 10]

In this code, the expression (value := number * 2 + 1) assigns the calculated value to the value variable while simultaneously checking if it is greater than 10. If the condition is true, the number is included in the resulting odd_numbers list.

The walrus operator enhances the readability and efficiency of list comprehensions by allowing you to assign values within the comprehension itself.

Deciding When Not to Use a List Comprehension

While list comprehensions are a powerful tool, there are situations where using them may not be the best choice. Let’s explore some scenarios where alternative approaches should be considered.

Watch Out for Nested Comprehensions

List comprehensions can become less readable and harder to understand when they are nested within each other. The complexity increases as the number of nested comprehensions grows, making the code more difficult to maintain.

In such cases, it may be better to use traditional for loops or break down the problem into separate steps using helper functions to improve clarity and maintainability.

Choose Generators for Large Datasets

List comprehensions generate the entire resulting list in memory at once. This can be problematic when working with large datasets that consume a significant amount of memory.

In such situations, using a generator comprehension can be a more memory-efficient solution. Generators produce values on the fly as requested, rather than creating the entire list upfront. This can greatly reduce memory usage and improve performance.

To convert a list comprehension into a generator comprehension, simply replace the square brackets with parentheses:

generator = (expression for item in iterable)

By choosing generators over list comprehensions, you can handle large datasets more effectively.

Profile to Optimize Performance

List comprehensions are generally efficient and performant. However, for complex operations or large datasets, it’s important to profile your code to identify potential bottlenecks and optimize performance.

Tools like the Python timeit module or more advanced profilers can help you analyze the execution time and memory usage of your list comprehensions. By pinpointing the areas that need improvement, you can optimize your code and make it more efficient.


In this tutorial, we have explored the power of Python list comprehensions and learned how to use them effectively. We have seen how list comprehensions can transform lists, filter values, remove duplicates, and even assign values using the walrus operator. We have also discussed scenarios where it is best to avoid using list comprehensions and opt for alternative approaches.

By mastering list comprehensions, you can write concise and expressive code that performs complex operations on lists, improving your productivity as a Python developer. So go ahead and start leveraging the full potential of list comprehensions in your Python projects. Happy coding!