Python Map Filter Reduce

map, filter, and reduce come from functional programming. They let you process collections in a declarative way.

    General idea:
    - map → transform
    - filter → select
    - reduce → combine
    
    ## map(): transform items
    
    ```python
    numbers = [1, 2, 3, 4, 5]
    squared = list(map(lambda x: x ** 2, numbers))
    print(squared)
    ```
    
    Equivalent with list comprehension:
    ```python
    squared = [x ** 2 for x in numbers]
    ```
    
    ## filter(): select items
    
    ```python
    numbers = [1,2,3,4,5,6,7,8,9,10]
    evens = list(filter(lambda x: x % 2 == 0, numbers))
    print(evens)
    ```
    
    Equivalent:
    ```python
    evens = [x for x in numbers if x % 2 == 0]
    ```
    
    ## reduce(): combine items
    
    ```python
    from functools import reduce
    
    numbers = [1, 2, 3, 4, 5]
    total = reduce(lambda x, y: x + y, numbers)
    print(total)
    ```
    
    ## Real examples
    
    ```python
    names = ["tom", "sarah", "mike"]
    upper = list(map(str.upper, names))
    
    words = ["hi", "hello", "hey", "goodbye"]
    long_words = list(filter(lambda w: len(w) > 3, words))
    
    nums = [2, 3, 4]
    product = reduce(lambda x, y: x * y, nums)
    
    print(upper, long_words, product)
    ```
    
    ## Graph: data flow
    
    ```mermaid
    flowchart LR
      A[Data] --> B[map: transform]
      B --> C[filter: select]
      C --> D[reduce: combine]
      D --> E[Single result]
    ```
    
    ## Best practice
    
    - Prefer list comprehensions for clarity
    - Use reduce only when logic is truly combining
    - Keep lambdas simple
    
    In the next lesson, you will master sorting in Python, including custom keys and multi-level sorting.