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List Comprehensions in Python

 

List Comprehensions in Python

List comprehensions are a concise way to create lists in Python. They allow you to generate new lists by applying an expression to each item in an existing iterable (like a list, tuple, or range) and optionally applying a condition to filter elements. List comprehensions can be more readable and faster than using traditional for loops to generate lists.

1. Basic Syntax of List Comprehension

The basic syntax of a list comprehension is:

new_list = [expression for item in iterable]
  • expression: The expression that evaluates to the value that will be added to the new list.
  • item: A variable representing the current item in the iterable.
  • iterable: The iterable (e.g., list, range, tuple) to loop over.

Example 1: List Comprehension to Create a New List

# Traditional loop
squares = []
for i in range(5):
    squares.append(i**2)

print(squares)

Output:

[0, 1, 4, 9, 16]

The same result using list comprehension:

squares = [i**2 for i in range(5)]
print(squares)

Output:

[0, 1, 4, 9, 16]

In this example, the list comprehension creates a new list by squaring each number from 0 to 4.

2. Adding a Condition to the List Comprehension

You can add an if condition to filter elements from the iterable. The condition is evaluated for each item, and only the items that satisfy the condition are included in the resulting list.

Example 2: List Comprehension with a Condition

# Create a list of even numbers
even_numbers = [i for i in range(10) if i % 2 == 0]
print(even_numbers)

Output:

[0, 2, 4, 6, 8]

Here, the if i % 2 == 0 condition filters out the odd numbers, resulting in a list of even numbers.

3. Using else in List Comprehensions

You can also use an else statement in a list comprehension. The else part applies when the condition is not met. The syntax for this is:

new_list = [expression_if_true if condition else expression_if_false for item in iterable]

Example 3: List Comprehension with else

# Create a list where numbers are squared if they are even, otherwise, they are doubled
numbers = [i if i % 2 == 0 else i * 2 for i in range(5)]
print(numbers)

Output:

[0, 2, 4, 6, 8]

In this example:

  • For even numbers, it keeps the number as is.
  • For odd numbers, it doubles them.

4. Nested List Comprehensions

List comprehensions can be nested. This means you can use one list comprehension inside another. This is useful when working with multi-dimensional lists, such as lists of lists.

Example 4: Nested List Comprehension

# Flatten a 2D list using a list comprehension
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
flattened = [item for sublist in matrix for item in sublist]
print(flattened)

Output:

[1, 2, 3, 4, 5, 6, 7, 8, 9]

In this example, the inner for loop iterates over each sublist, and the outer for loop iterates over each item in the sublist, flattening the 2D list into a 1D list.

5. List Comprehensions with Multiple Conditions

You can combine multiple conditions in a list comprehension to further filter the elements.

Example 5: List Comprehension with Multiple Conditions

# List of numbers divisible by both 2 and 3
divisible_by_both = [i for i in range(20) if i % 2 == 0 and i % 3 == 0]
print(divisible_by_both)

Output:

[0, 6, 12, 18]

In this example, the list comprehension filters out the numbers that are divisible by both 2 and 3.

6. Applying Functions to List Comprehensions

You can use functions inside list comprehensions to modify the items as they are added to the new list.

Example 6: List Comprehension with a Function

# Create a list of uppercase versions of each word
words = ["hello", "world", "python"]
uppercase_words = [word.upper() for word in words]
print(uppercase_words)

Output:

['HELLO', 'WORLD', 'PYTHON']

Here, the upper() function is applied to each word in the list, and the result is stored in the new list.

7. Performance Benefits of List Comprehensions

List comprehensions are often faster than using traditional for loops to create lists. This is because they are optimized for creating lists, and there’s less overhead involved in the loop construction.

Example: Performance Comparison

# Using list comprehension
list_comp = [i**2 for i in range(1000000)]

# Using for loop
list_loop = []
for i in range(1000000):
    list_loop.append(i**2)

The list comprehension is typically faster because it is implemented in C and optimized for performance, while the for loop involves more overhead in Python’s interpreter.

Summary of List Comprehensions

  • Basic syntax: [expression for item in iterable]
  • Conditionals: You can add if conditions to filter items: [expression for item in iterable if condition].
  • Using else: The syntax for else in a list comprehension is [expression_if_true if condition else expression_if_false for item in iterable].
  • Nested comprehensions: You can nest list comprehensions to handle multi-dimensional data: [item for sublist in iterable for item in sublist].
  • Multiple conditions: You can combine multiple conditions in the comprehension.
  • Functions: List comprehensions can apply functions to each element as it is being added to the new list.
  • Performance: List comprehensions are often faster than traditional loops for creating lists.

List comprehensions are a powerful feature in Python that can make your code more concise, readable, and efficient, especially when working with large datasets or performing complex transformations.

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