List Comprehensions for New Lists

From for Loop to List Comprehension

Let's start by revisiting a task from our previous lesson: creating a new list. Imagine you have a list of numbers and you want to create a new list containing the square of each number.

Using a for loop, you would write this:

numbers = [1, 2, 3, 4, 5]
squares = []  # 1. Initialize an empty list

for number in numbers:  # 2. Loop through the original list
    squares.append(number * number)  # 3. Append the new value

print(squares)



# Output: [1, 4, 9, 16, 25]

A list comprehension achieves the exact same result in a single line:

numbers = [1, 2, 3, 4, 5]
squares = [number * number for number in numbers] # All three steps in one!

print(squares)



# Output: [1, 4, 9, 16, 25]

This structure is incredibly useful. The following video gives a great introduction to how this conversion from a for loop to a list comprehension works.

As you saw, the basic syntax follows a clear pattern. This image breaks it down visually.

Adding Conditional Logic

The real power of list comprehensions becomes apparent when you start adding conditional logic. You can use them to both filter data and transform data differently based on a condition.

Filtering with an if Statement

Let's say you want to create a list of squares, but only for the even numbers in your original list. In a list comprehension, you can add an if clause at the end to act as a filter.

The syntax is: [expression for item in iterable if condition]

numbers = [1, 2, 3, 4, 5, 6, 7, 8]
even_squares = [num * num for num in numbers if num % 2 == 0]

print(even_squares)



# Output: [4, 16, 36, 64]

The next video provides an excellent visual walkthrough of how Python processes this logic: it loops, then filters, and only then applies the transformation.

Transforming with if-else

What if you want to apply a different action depending on the item? For example, categorize numbers as "small" or "large". For this, you use an if-else structure, but the syntax is different. The if-else block moves to the beginning of the comprehension.

The syntax is: [value_if_true if condition else value_if_false for item in iterable]

numbers = [25, 80, 12, 55, 98, 30]
categories = ['High' if num > 50 else 'Low' for num in numbers]

print(categories)



# Output: ['Low', 'High', 'Low', 'High', 'High', 'Low']

This is a critical distinction:

• if at the end is for filtering (skipping items).
• if-else at the beginning is for transforming (changing every item).

This image shows both variations side-by-side.

For a more detailed explanation of these two conditional structures, the following article is very clear.

A Note on Readability

You can even nest loops inside a list comprehension, but be careful. While it's possible to write very complex one-liners, your goal should always be clear and maintainable code. If a list comprehension becomes too long or hard to read, a standard for loop is often a better choice.

This final video clip demonstrates a nested comprehension and gives some excellent advice on when to avoid them.

Put it into Practice

Time to apply what you've learned. Here are a few exercises based on common data analysis scenarios.

Exercise 1: Data Transformation
You have a list of temperatures in Celsius. Create a new list with the temperatures converted to Fahrenheit.
The formula is: $F=(C\times 9/5)+32$.

temps_celsius = [22.5, 31.0, 18.2, 25.1, 15.7]




# Your code here
# temps_fahrenheit = ...




# Solution
temps_fahrenheit = [(temp * 9/5) + 32 for temp in temps_celsius]
print(temps_fahrenheit)

Exercise 2: Filtering and Transformation
You have a list of product prices. Create a new list containing the prices of items that cost more than $50, with a 10% "luxury tax" added.

prices_usd = [120, 45, 88, 32, 150, 51]




# Your code here
# taxed_luxury_prices = ...




# Solution
taxed_luxury_prices = [price * 1.10 for price in prices_usd if price > 50]
print(taxed_luxury_prices)

Exercise 3: Conditional Transformation (if-else)
You have a list of employee work hours for a week. Create a list that labels each entry as 'Overtime' if the hours are over 40, and 'Standard' otherwise.

work_hours = [38, 45, 40, 48, 35]




# Your code here
# hour_labels = ...




# Solution
hour_labels = ['Overtime' if hours > 40 else 'Standard' for hours in work_hours]
print(hour_labels)

Conclusion

Excellent work! You've just added a powerful and "Pythonic" tool to your data analysis toolkit. Mastering list comprehensions will make your code more concise, efficient, and readable.

Key Takeaways:

• List comprehensions are a compact way to create a new list by processing an existing iterable.
• The basic syntax is [expression for item in iterable].
• To filter items, add an if statement at the end: [... for ... if condition].
• To transform items differently based on a condition, use an if-else block at the beginning: [value_if_true if condition else value_if_false for ...].
• While powerful, always prioritize code readability. A simple for loop is better than a complex, unreadable comprehension.

Preview of the Next Lesson:

So far, we've written code that runs from top to bottom. But what if you need to perform the same set of steps multiple times in your analysis? In our next lesson, we'll cover how to define and call functions to organize reusable code. This will allow you to package logic—like the data cleaning steps or calculations you've been learning—into blocks that you can use again and again.