For Loop Examples

List Comprehensions

List comprehensions provide a concise way to create lists in Python. They’re essentially a shorthand for a for loop combined with an optional conditional statement.

Basic Syntax:

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

• expression: What you want to do with each item. This can be a simple transformation or a more complex calculation.
• item: A variable representing each element in the iterable.
• iterable: Something you can iterate over (like a list, tuple, range, etc.).
• if condition: (Optional) A filter to include only items that meet a specific criteria.

Examples:

1. Squaring numbers:

numbers = 1, 2, 3, 4, 5
squares = [x**2 for x in numbers]]  # Output: 1, 4, 9, 16, 25

2. Filtering even numbers:

numbers = 1, 2, 3, 4, 5, 6
even_numbers = [x for x in numbers if x % 2 == 0]] # Output: 2, 4, 6

3. String manipulation:

words = ["hello", "world", "python"]]
uppercase_words = [word.upper() for word in words]] # Output: ['HELLO', 'WORLD', 'PYTHON']]

4. Nested List Comprehension (Nested Loops): Creating a matrix:

matrix = [i*j for j in range(3](./../i*j-for-j-in-range(3/))]] for i in range(3)]] # Output: [0, 0, 0](./../0,-0,-0/), [0, 1, 2, [0, 2, 4

Advantages:

• Readability: Often more concise and easier to read than equivalent for loops.
• Efficiency: Can be slightly faster than traditional loops in some cases, especially for simple operations.

When NOT to use List Comprehensions:

• Complex logic: If your logic involves multiple nested loops or very complex conditional statements, a traditional for loop might be clearer.
• Side effects: Avoid using list comprehensions if you need to perform actions that have side effects (like modifying external variables) within the comprehension. This can lead to less readable and less maintainable code.

Iterable Objects