Python Arrays, Recursion and Anonymous Functions

I. ARRAYS IN PYTHON Introduction to Arrays in Python Arrays are data structures that store multiple values of the same data type in a single variable. Arrays are useful when you need to store and manipulate a collection of data. Python doesn't have a built-in array data type like some other lan...

I. ARRAYS IN PYTHON Introduction to Arrays in Python Arrays are data structures that store multiple values of the same data type in a single variable. Arrays are useful when you need to store and manipulate a collection of data. Python doesn't have a built-in array data type like some other languages. However, you can the `array` module to create arrays. Python Array Module The array module allows creating arrays with constraints on data types. Supported Data Types - `i`: signed integer - `I`: unsigned integer - `f`: floating point - `u`: Unicode character (deprecated) Creating an Array `array.array(typecode, values_list)` Example import array int_array = array.array('i', [1, 2, 3, 4]) float_array = array.array('f', [1.1, 2.2, 3.3, 4.4]) Indexing and slicing in arrays in Python: Indexing allows you to access a single element in an array. Syntax `array_name[index]` Example import array my_array = array.array('i', [1, 2, 3, 4, 5]) print(my_array) # Output: 1 Slicing allows you to access a subset of elements in an array. Syntax `array_name[start:stop:step]` Example import array my_array = array.array('i', [1, 2, 3, 4, 5]) print(my_array[1:3]) # Output: array('i', [2, 3]) Explanation - `start`: The starting index of the slice. - `stop`: The ending index of the slice. - `step`: The increment between elements in the slice. II. RECURSION IN PYTHON Introduction to Recursion Recursion is a programming technique where a function calls itself repeatedly until it reaches a base case that stops the recursion. Recursion is useful for solving problems that have a recursive structure, such as tree or graph traversals, and dynamic programming. Recursive Functions It has two main components: 1. *Base case*: A trivial case that can be solved directly, stopping the recursion. 2. *Recursive case*: A case that breaks down the problem into smaller sub-problems, solved by calling the function itself. Examples of Recursive Algorithms Factorial The factorial of a number `n` (denoted as `n!`) is the product of all positive integers less than or equal to `n`. n! = n * (n-1) * (n-2) *... * 1 Here's a recursive implementation of the factorial function: def factorial(n): if n == 0: # base case return 1 else: return n * factorial(n-1) # recursive case Fibonacci Series The Fibonacci sequence is a series of numbers where each number is the sum of the two preceding ones, usually starting with 0 and 1. def fibonacci(n): if n == 0 or n == 1: # base case return n else: return fibonacci(n-1) + fibonacci(n-2) # recursive case III. ANONYMOUS FUNCTIONS Anonymous functions, also known as lambda functions, are small, single-purpose functions that can be defined without a name. They are called "anonymous" because they are not declared with a name. Syntax of Anonymous Functions The syntax of an anonymous function in Python is: lambda arguments: expression Where: - `arguments` is a comma-separated list of variables that will be passed to the function. - `expression` is the code that will be executed when the function is called. Example of an Anonymous Function Here's an example of an anonymous function that takes a single argument and returns its square: square = lambda x: x ** 2 print(square(5)) # Output: 25