PythonDairy

 An algorithm is a finite sequence of well-defined steps used to solve a problem or perform a computation. However, not all algorithms are created equal. Some are faster or more efficient than others for the same task. Evaluating an algorithm’s performance is crucial to determine its suitability for a given problem, especially when working with large datasets. Why Evaluate Algorithm Performance? Scalability : To understand how an algorithm performs as input size grows. Efficiency : To compare multiple algorithms and choose the most efficient one. Resource Usage : To analyze memory and processing power requirements. Real-World Constraints : Efficient algorithms are critical in applications like real-time systems, where delays can have serious consequences. Algorithm Complexity Algorithm complexity measures the amount of time and space an algorithm consumes relative to the size of its input. Time Complexity : Measures the number of basic operations or steps an algorithm takes to c...

  Hash Tables and Hashing A hash table is a data structure that maps keys to values for efficient data retrieval. The underlying concept is based on hashing , which involves using a hash function to compute an index (or hash code) for each key. This index determines where the value associated with the key will be stored in the table. Key Features of Hash Tables Fast Lookup : Hash tables offer average O(1) time complexity for lookups, insertions, and deletions. Key-Value Pair Storage : Data is stored as key-value pairs. Dynamic Size : Many implementations dynamically resize the hash table as the number of elements grows. How Hashing Works A hash function converts a key into a numerical hash code. The hash code is mapped to an index in an array (or bucket) of fixed size. If two keys produce the same hash code (a collision ), a collision resolution strategy is used, such as: Chaining : Store multiple values in a linked list or similar structure at the same index. Open Addressing...

 A tree is a hierarchical data structure consisting of nodes. The topmost node is the root , and each node may have child nodes. Trees are commonly used in scenarios where data needs to be represented hierarchically, such as file systems, organizational structures, and more. Terminology: Root : The topmost node. Parent/Child : Nodes are connected; the higher node is the parent, and the lower one is the child. Leaf : A node with no children. Depth : The level of a node in the tree. Height : The longest path from a node to its leaf. Binary Tree A binary tree is a tree where each node has at most two children, referred to as the left and right child. Example of a Binary Tree Python Implementation: class Node :     def __init__ ( self , value ):         self . value = value         self . left = None         self . right = None # Example Tree root = Node ( 1 ) root . left = Node ( 2 ) root . right = Nod...

 Stacks and queues are essential data structures in programming, each with unique characteristics and uses. 1.Stack (LIFO - Last In, First Out) A stack is a LIFO (Last In, First Out) data structure, where the last element added is the first one removed. Think of it like a stack of plates: you add to the top, and you also remove from the top. In Python, we can use a list to implement a stack because lists support adding and removing items from the end in constant time, which aligns with stack operations. Stack Operations Push : Add an item to the stack. Pop : Remove the last item added. Peek : Get the value of the last item without removing it. IsEmpty : Check if the stack is empty. Here's a simple implementation of a stack using a list in Python: class Stack :     def __init__ ( self ):         self . stack = []     def push ( self , item ):         self . stack . append ( item )     def pop ( self ): ...