9.5 List comprehensions

List comprehensions in Python offer a concise way to create new lists based on existing ones. They combine the power of loops, conditional statements, and data transformation into a single line of code, making your programs more efficient and readable.

These versatile tools allow you to filter elements, apply complex transformations, and even work with nested loops. By mastering list comprehensions, you'll streamline your code and enhance your ability to manipulate data effectively in Python.

List Comprehensions

List creation with comprehensions

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• List comprehensions concisely create new lists based on existing lists or iterable objects
• Basic syntax:

  [[expression](https://www.fiveableKeyTerm:Expression) [for](https://www.fiveableKeyTerm:for) item in iterable [if](https://www.fiveableKeyTerm:If) condition]

  • expression

    transforms each item in the iterable

  • item

    represents each element in the iterable

  • iterable

    is the source list or iterable object
  • Optional

    if condition

    filters which items are included
• Create a list of squares from 1 to 5
  • Traditional for loop:

    squares = [[]](https://www.fiveableKeyTerm:[])
    for i in range(1, 6):
        squares.append(i ** 2)
    

  • List comprehension:

    squares = [i ** 2 for i in range(1, 6)]
    

• List comprehensions work with nested loops
  • Create list of tuples with all combinations of elements from two lists (

    list1

    and

    list2

    )

    combinations = [(x, y) for x in list1 for y in list2]
    

Filtering elements with comprehensions

• List comprehensions filter elements from the source list using an optional condition
• Condition specified with

  if

  clause after

  for

  loop
• Only elements satisfying the condition are included
• Create list of even numbers from existing list (

  numbers

  )
  • Traditional for loop with conditional:

    even_numbers = []
    for num in numbers:
        if num % 2 == 0:
            even_numbers.append(num)
    

  • List comprehension with filter condition:

    even_numbers = [num for num in numbers if num % 2 == 0]
    

Data transformation in comprehensions

• List comprehensions use complex expressions to transform data in the new list
• Expression can include function calls, math operations, string manipulation, or valid Python expressions
• Create list of string lengths from existing list (

  words

  )
  • Traditional for loop with

    len()

    function:

    word_lengths = []
    for word in words:
        word_lengths.append(len(word))
    

  • List comprehension with complex expression:

    word_lengths = [len(word) for word in words]
    

• List comprehensions include conditional expressions using ternary operator
  • Syntax:

    expression_if_true if condition else expression_if_false

  • Create list with squares of even numbers and cubes of odd numbers from list (

    numbers

    )

    result = [x ** 2 if x % 2 == 0 else x ** 3 for x in numbers]
    

Advanced Comprehension Concepts

• Generator expressions: Similar to list comprehensions but create generators for memory efficiency
• Dictionary comprehensions: Create dictionaries using a syntax similar to list comprehensions
• Lambda functions can be used within comprehensions for more complex operations
• Performance considerations: List comprehensions are generally faster than equivalent for loops
• Readability: While concise, complex comprehensions may sacrifice readability for brevity