Standardize Repository Structure and Documentation

Arrays/README.md

@@ -4,10 +4,10 @@ This directory contains Python implementations of common array-based algorithms
 
 ## Contents
 
-- [Anagram Check (Sorted Solution)](Anagram_Check_Sorted_Sol.py): Checks if two strings are anagrams by comparing their sorted versions.
-- [Anagram Check (Manual Solution)](Anagram_Check_manual_Sol.py): Checks if two strings are anagrams using a hash table (dictionary) to count character frequencies.
-- [Array Find Missing Element (XOR Solution)](ArrayFindTheMissingElement_XOR_sol.py): Efficiently finds a missing element in a shuffled array using bitwise XOR.
-- [Array Find Missing Element (Brute Force Solution)](ArrayFindTheMissingElement_brute_force_sol.py): Finds a missing element by sorting both arrays and comparing them.
-- [Array Find Missing Element (Hash Table Solution)](ArrayFindTheMissingElement_hash_table_sol.py): Finds a missing element using a hash table (dictionary) to track element counts.
-- [Array Find Missing Element (Sum/Subtract Solution)](ArrayFindTheMissingElement_takingSumandSubtract_sol.py): Finds a missing element by calculating the difference between the sums of the two arrays.
+- [Anagram Check (Sorted Solution)](AnagramCheckSortedSol.py): Checks if two strings are anagrams by comparing their sorted versions. Time Complexity: $O(n \log n)$.
+- [Anagram Check (Manual Solution)](AnagramCheckManualSol.py): Checks if two strings are anagrams using a hash table (dictionary) to count character frequencies. Time Complexity: $O(n)$.
+- [Array Find Missing Element (XOR Solution)](ArrayFindTheMissingElementXORSol.py): Efficiently finds a missing element in a shuffled array using bitwise XOR. Time Complexity: $O(n)$.
+- [Array Find Missing Element (Brute Force Solution)](ArrayFindTheMissingElementBruteForceSol.py): Finds a missing element by sorting both arrays and comparing them. Time Complexity: $O(n \log n)$.
+- [Array Find Missing Element (Hash Table Solution)](ArrayFindTheMissingElementHashTableSol.py): Finds a missing element using a hash table (dictionary) to track element counts. Time Complexity: $O(n)$.
+- [Array Find Missing Element (Sum/Subtract Solution)](ArrayFindTheMissingElementSumSol.py): Finds a missing element by calculating the difference between the sums of the two arrays. Time Complexity: $O(n)$.
 - [Array Pair Sum Solution](ArrayPairSumSol.py): Finds all unique pairs in an array that sum up to a specific value $k$ using a set for $O(n)$ complexity.

Deque/README.md

@@ -0,0 +1,7 @@
+# Double-Ended Queue (Deque)
+
+This directory contains Python implementations of double-ended queue (deque) data structures.
+
+## Contents
+
+- [Deque Implementation](DequeImple.py): Implementation of a deque using a Python list. Time Complexity: `addFront`/`removeFront` $O(1)$, `addRear`/`removeRear` $O(n)$ due to list shifting.

ErrorHandling/README.md

@@ -0,0 +1,7 @@
+# Error Handling
+
+This directory contains examples of error and exception handling in Python.
+
+## Contents
+
+- [Error and Exceptions](ErrorExceptions.py): Demonstrates the use of `try`, `except`, `else`, and `finally` blocks for robust error handling.

GraphAlgorithms/README.md

@@ -4,9 +4,9 @@ This directory contains Python implementations of common graph-based algorithms
 
 ## Contents
 
-- [Adjacency List Implementation](AdjacencyListGraphImple.py): Implements the Graph Abstract Data Type (ADT) using an adjacency list (dictionaries in Python). Includes `Vertex` and `Graph` classes.
-- [Breadth First Search (BFS)](BFS.py): Implements BFS to solve the Word Ladder problem, finding the shortest transformation path between words.
-- [General Depth First Search (DFS)](DFSGeneral.py): Provides a general implementation of DFS, including discovery and finish times for vertices.
-- [DFS - Knight's Tour Problem](DFSImpleTheKnightsTourProblem.py): Another implementation of DFS specifically tailored to the Knight's Tour puzzle.
-- [The Knight's Tour Problem](TheKnightsTourProblem.py): Focuses on generating the knight's move graph and solving the tour using DFS and backtracking.
-- [Word Ladder Problem](WordLadderProblem.py): Specifically focuses on building the word ladder graph where edges connect words that differ by only one letter.
+- [Adjacency List Implementation](AdjacencyListGraphImple.py): Implements the Graph Abstract Data Type (ADT) using an adjacency list. Time Complexity: $O(1)$ to add vertex, $O(1)$ to add edge.
+- [Breadth First Search (BFS)](BFS.py): Implements BFS to solve the Word Ladder problem. Time Complexity: $O(V + E)$.
+- [General Depth First Search (DFS)](DFSGeneral.py): Provides a general implementation of DFS. Time Complexity: $O(V + E)$.
+- [DFS - Knight's Tour Problem](DFSImpleTheKnightsTourProblem.py): Specifically tailored DFS for the Knight's Tour puzzle. Time Complexity: $O(k^N)$.
+- [The Knight's Tour Problem](TheKnightsTourProblem.py): Generating the knight's move graph and solving the tour.
+- [Word Ladder Problem](WordLadderProblem.py): Building the word ladder graph where edges connect words that differ by only one letter.

LinkedLists/README.md

@@ -4,8 +4,8 @@ This directory contains Python implementations of various types of linked lists
 
 ## Contents
 
-- [Singly Linked List Implementation](SingleLinkedListImple.py): Basic implementation of a singly linked list node and basic linkage.
-- [Doubly Linked List Implementation](DoublyLinkedListImple.py): Basic implementation of a doubly linked list node with `prev` and `next` pointers.
-- [Singly Linked List Cycle Check](SinglyLinkedListCycleCheckImple.py): Implements Floyd's Cycle-Finding Algorithm (two pointers) to detect cycles in a linked list.
+- [Singly Linked List Implementation](SinglyLinkedListImple.py): Basic implementation of a singly linked list node and basic linkage. Time Complexity: Node linkage $O(1)$.
+- [Doubly Linked List Implementation](DoublyLinkedListImple.py): Basic implementation of a doubly linked list node with `prev` and `next` pointers. Time Complexity: Node linkage $O(1)$.
+- [Singly Linked List Cycle Check](SinglyLinkedListCycleCheckImple.py): Implements Floyd's Cycle-Finding Algorithm (two pointers) to detect cycles in a linked list. Time Complexity: $O(n)$.
 - [Linked List Reversal](LinkedListReversal.py): Reverses a singly linked list in-place in $O(n)$ time.
-- [Nth to Last Node](LinkedListNthToLastNode.py): Finds the $n$-th to last node in a singly linked list using two pointers.
+- [Nth to Last Node](LinkedListNthToLastNode.py): Finds the $n$-th to last node in a singly linked list using two pointers. Time Complexity: $O(n)$.

LinkedLists/SinglyLinkedListCycleCheckImple.py

@@ -19,10 +19,10 @@ def __init__(self, value):
         self.value = value
         self.nextnode = None
 
-    def cycle_check(node):
-        # Set two pointers initialize to passed node
-        pt1 = node
-        pt2 = node
+    def cycle_check(self):
+        # Set two pointers initialize to self
+        pt1 = self
+        pt2 = self
         # loop through end of the list
         while pt2 != None and pt2.nextnode != None:
             pt1 = pt1.nextnode
@@ -46,12 +46,4 @@ def cycle_check(node):
 print (b.value)
 print (c.value)
 
-# Since cycle_check is a method but it doesn't use self and is defined inside class
-# it should be called on an instance or changed to static method.
-# In its current definition it behaves like a regular method but is missing 'self'.
-# Actually it is defined as def cycle_check(node): which means it takes one arg.
-# If called as LinkedListNode.cycle_check(a) it should work if it was just a function.
-
 print(a.cycle_check())
-
-

Queues/README.md

@@ -1,8 +1,8 @@
 # Queues
 
-This directory contains Python implementations of the Queue data structure.
+This directory contains Python implementations of queue data structures.
 
 ## Contents
 
-- [Queue Implementation](QueueImple.py): Basic implementation of a FIFO (First-In-First-Out) queue using a Python list. Includes `enqueue`, `dequeue`, `isEmpty`, and `size` methods.
-- [Queue with Two Stacks](QueueWith2StacksImple.py): Implements a queue using two stacks (represented by Python lists) to achieve FIFO behavior.
+- [Queue Implementation](QueueImple.py): Basic implementation of a queue using a Python list. Time Complexity: `enqueue` $O(n)$, `dequeue` $O(1)$.
+- [Queue with Two Stacks](QueueWith2StacksImple.py): Implementing a queue using two stacks. Time Complexity: Amortized $O(1)$ for both operations.

README.md

@@ -67,7 +67,7 @@ Most scripts in this repository are standalone and can be executed directly:
 
 ```bash
 # Run any Python script
-python3 Arrays/Anagram_Check_Sorted_Sol.py
+python3 Arrays/AnagramCheckSortedSol.py
 
 # Or run from the repo root
 python3 Sorting/BubbleSortImple.py
@@ -80,15 +80,15 @@ python3 Sorting/BubbleSortImple.py
 ```
 .
 ├── Arrays/              # 🔤 Array-based problems and algorithms
-├── Error-debug/         # ⚠️ Error handling and debugging examples
+├── Deque/               # 🔄 Double-ended queue
+├── ErrorHandling/       # ⚠️ Error handling and debugging examples
 ├── GraphAlgorithms/     # 🗺️ Graph traversal (BFS, DFS) and pathfinding
 ├── LinkedLists/         # 🔗 Singly and Doubly Linked Lists
 ├── Queues/              # 📦 Queue implementations (FIFO)
 ├── Recursion/           # 🔀 Recursive problems and Dynamic Programming
 ├── Sorting/             # 📊 Common sorting algorithms
 ├── Stacks/              # 📚 Stack implementations and applications
 ├── Trees/               # 🌳 Binary Trees, BSTs, Heaps, and Traversals
-├── deque/               # 🔄 Double-ended queue
 ├── CONTRIBUTING.md      # 🤝 Contribution guidelines
 ├── LICENSE              # 📄 MIT License
 └── README.md            # 📖 This file
@@ -100,21 +100,21 @@ python3 Sorting/BubbleSortImple.py
 
 ### Arrays 🔤
 Common array-based algorithms and manipulations.
-- [Anagram Check](Arrays/): [Sorted](Arrays/Anagram_Check_Sorted_Sol.py) & [Manual](Arrays/Anagram_Check_manual_Sol.py) solutions
+- [Anagram Check](Arrays/): [Sorted](Arrays/AnagramCheckSortedSol.py) & [Manual](Arrays/AnagramCheckManualSol.py) solutions
 - [Array Pair Sum](Arrays/ArrayPairSumSol.py): Find pairs that sum to $k$
-- [Find Missing Element](Arrays/): [XOR](Arrays/ArrayFindTheMissingElement_XOR_sol.py), [Brute Force](Arrays/ArrayFindTheMissingElement_brute_force_sol.py), [Hash Table](Arrays/ArrayFindTheMissingElement_hash_table_sol.py), & [Sum](Arrays/ArrayFindTheMissingElement_takingSumandSubtract_sol.py) approaches
+- [Find Missing Element](Arrays/): [XOR](Arrays/ArrayFindTheMissingElementXORSol.py), [Brute Force](Arrays/ArrayFindTheMissingElementBruteForceSol.py), [Hash Table](Arrays/ArrayFindTheMissingElementHashTableSol.py), & [Sum](Arrays/ArrayFindTheMissingElementSumSol.py) approaches
 
 ### Linked Lists 🔗
 Implementations and problems involving linked structures.
-- [Singly Linked List](LinkedLists/SingleLinkedListImple.py) & [Doubly Linked List](LinkedLists/DoublyLinkedListImple.py)
+- [Singly Linked List](LinkedLists/SinglyLinkedListImple.py) & [Doubly Linked List](LinkedLists/DoublyLinkedListImple.py)
 - [Cycle Detection](LinkedLists/SinglyLinkedListCycleCheckImple.py): Detect cycles using two pointers (Floyd's algorithm)
 - [Reverse Linked List](LinkedLists/LinkedListReversal.py): In-place reversal
 - [Nth to Last Node](LinkedLists/LinkedListNthToLastNode.py): Find the $n$-th node from the end
 
 ### Stacks 📚
 LIFO (Last-In-First-Out) data structures.
 - [Stack Implementation](Stacks/StackImple.py): Basic operations (push, pop, peek)
-- [Balanced Parentheses](Stacks/BalanceParenthlessCheckImple.py): Check for balanced brackets using a stack
+- [Balanced Parentheses](Stacks/BalanceParenthesesCheckImple.py): Check for balanced brackets using a stack
 
 ### Queues 📦
 FIFO (First-In-First-Out) data structures.
@@ -123,7 +123,7 @@ FIFO (First-In-First-Out) data structures.
 
 ### Deque 🔄
 Double-ended queue operations.
-- [Deque Implementation](deque/DequeImple.py): Operations at both ends
+- [Deque Implementation](Deque/DequeImple.py): Operations at both ends
 
 ### Trees 🌳
 Hierarchical data structures.
@@ -133,7 +133,7 @@ Hierarchical data structures.
 - [Binary Heap](Trees/BinaryHeapImple.py): Min-heap implementation
 - [Tree Traversals](Trees/TreeLevelOrderPrintImple.py): Level order (BFS) printing
 - [Trim BST](Trees/TrimBinarySearchTreeImple.py): Keep nodes within a range
-- [Tree Representations](Trees/): [Nodes & References](Trees/TreeRepresentationWithNodesReferences.py) & [List of Lists](Trees/buildTreeTest.py)
+- [Tree Representations](Trees/): [Nodes & References](Trees/TreeRepresentationWithNodesReferences.py) & [List of Lists](Trees/BuildTreeTest.py)
 
 ---
 
@@ -144,7 +144,7 @@ Algorithms for arranging elements in order.
 - [Bubble Sort](Sorting/BubbleSortImple.py) - $O(n^2)$
 - [Selection Sort](Sorting/SelectionSortImple.py) - $O(n^2)$
 - [Insertion Sort](Sorting/InsertionSortImple.py) - $O(n^2)$
-- [Shell Sort](Sorting/ShellSortImple.py) - $O(n \log n)$
+- [Shell Sort](Sorting/ShellSortImple.py) - $O(n^2)$
 - [Merge Sort](Sorting/MergeSortImple.py) - $O(n \log n)$
 - [Quick Sort](Sorting/QuickSortImple.py) - $O(n \log n)$ average
 
@@ -168,7 +168,7 @@ Algorithms for graph traversal and pathfinding.
 
 ## ⚠️ Error Handling & Debugging
 
-- [Error and Exceptions](Error-debug/ErrorExceptions.py): Demonstrates `try`, `except`, `else`, and `finally` blocks for robust error handling.
+- [Error and Exceptions](ErrorHandling/ErrorExceptions.py): Demonstrates `try`, `except`, `else`, and `finally` blocks for robust error handling.
 
 ---
 

Sorting/README.md

@@ -5,8 +5,8 @@ This directory contains Python implementations of various sorting algorithms wit
 ## Contents
 
 - [Bubble Sort](BubbleSortImple.py): Implementation of Bubble Sort with $O(n^2)$ complexity.
-- [Selection Sort](SelectionSortImple.py): Implementation of Selection Sort, improving on Bubble Sort by making only one exchange per pass.
-- [Insertion Sort](InsertionSortImple.py): Implementation of Insertion Sort, maintaining a sorted sublist.
-- [Shell Sort](ShellSortImple.py): Implementation of Shell Sort (diminishing increment sort), improving on Insertion Sort.
+- [Selection Sort](SelectionSortImple.py): Implementation of Selection Sort, improving on Bubble Sort by making only one exchange per pass. Time Complexity: $O(n^2)$.
+- [Insertion Sort](InsertionSortImple.py): Implementation of Insertion Sort, maintaining a sorted sublist. Time Complexity: $O(n^2)$.
+- [Shell Sort](ShellSortImple.py): Implementation of Shell Sort (diminishing increment sort). Time Complexity: $O(n^2)$ (worst case).
 - [Merge Sort](MergeSortImple.py): A recursive "divide and conquer" algorithm with $O(n \log n)$ complexity.
-- [Quick Sort](QuickSortImple.py): Implementation of Quick Sort (partition exchange sort), using divide and conquer in-place.
+- [Quick Sort](QuickSortImple.py): Implementation of Quick Sort (partition exchange sort), using divide and conquer in-place. Time Complexity: $O(n \log n)$ average, $O(n^2)$ worst case.

Stacks/README.md

@@ -1,8 +1,8 @@
 # Stacks
 
-This directory contains Python implementations of the Stack data structure and its applications.
+This directory contains Python implementations of stack-based data structures and algorithms.
 
 ## Contents
 
-- [Stack Implementation](StackImple.py): Basic implementation of a LIFO (Last-In-First-Out) stack using a Python list. Includes `push`, `pop`, `peek`, `isEmpty`, and `size` methods.
-- [Balanced Parentheses Check](BalanceParenthlessCheckImple.py): Uses a stack to check if a string of opening and closing parentheses (round, square, and curly) is balanced.
+- [Stack Implementation](StackImple.py): Basic implementation of a stack with push, pop, and peek operations. Time Complexity: All operations $O(1)$.
+- [Balance Parentheses Check](BalanceParenthesesCheckImple.py): Algorithm to check if parentheses in a string are balanced using a stack. Time Complexity: $O(n)$.

Trees/README.md

@@ -1,23 +1,16 @@
 # Trees
 
-This directory contains Python implementations of various tree-based data structures and algorithms.
+This directory contains Python implementations of tree-based data structures and algorithms.
 
 ## Contents
 
-### Binary Search Trees (BST)
-- [Binary Search Tree Implementation](BinarySearchTreesImple.py): A comprehensive implementation of a BST with `TreeNode` and `BinarySearchTree` classes, including insertion, deletion, and search.
-- [Validate BST (Solution 1)](BinarySearchTreeCheckImpleSol1.py): Validates a BST by performing an in-order traversal and checking if the resulting values are sorted.
-- [Validate BST (Solution 2)](BinarySearchTreeCheckImpleSol2.py): Validates a BST by keeping track of the minimum and maximum allowable values for each node.
-- [Trim a BST](TrimBinarySearchTreeImple.py): Trims a BST so that all node values fall within a specified range $[min, max]$.
-
-### Search Algorithms
-- [Binary Search (Iterative)](BinarySearchImple.py): Iterative implementation of the binary search algorithm on a sorted list.
-- [Binary Search (Recursive)](BinarySearchRecursiveImple.py): Recursive implementation of the binary search algorithm.
-
-### Heaps
-- [Binary Heap Implementation](BinaryHeapImple.py): Implements a min-heap using a recursive approach, including `insert`, `delMin`, and `buildHeap`.
-
-### Tree Representations & Traversals
-- [Nodes and References Representation](TreeRepresentationWithNodesReferences.py): A simple implementation of a binary tree using a class-based nodes and references approach.
-- [List of Lists Representation](buildTreeTest.py): Demonstrates building and manipulating a tree using a "list of lists" approach.
-- [Tree Level Order Print](TreeLevelOrderPrintImple.py): Prints a binary tree in level order (breadth-first) using a queue, with each level on a new line.
+- [Binary Heap Implementation](BinaryHeapImple.py): Implementation of a min-heap. Time Complexity: $O(\log n)$ for insert/delete.
+- [Binary Search (Iterative)](BinarySearchImple.py): Iterative implementation of the binary search algorithm. Time Complexity: $O(\log n)$.
+- [Binary Search (Recursive)](BinarySearchRecursiveImple.py): Recursive implementation of the binary search algorithm. Time Complexity: $O(\log n)$.
+- [Binary Search Tree Implementation](BinarySearchTreesImple.py): A full BST implementation with search, insert, and delete. Time Complexity: $O(\log n)$ average, $O(n)$ worst case.
+- [BST Validation (Solution 1)](BinarySearchTreeCheckImpleSol1.py): Validating a BST using in-order traversal. Time Complexity: $O(n)$.
+- [BST Validation (Solution 2)](BinarySearchTreeCheckImpleSol2.py): Validating a BST using range checks. Time Complexity: $O(n)$.
+- [Build Tree Test](BuildTreeTest.py): Demonstration of tree representations using lists.
+- [Tree Level Order Print](TreeLevelOrderPrintImple.py): Printing a tree in level-order (BFS). Time Complexity: $O(n)$.
+- [Tree Representation (Nodes and References)](TreeRepresentationWithNodesReferences.py): Classic OOP tree representation.
+- [Trim Binary Search Tree](TrimBinarySearchTreeImple.py): Trimming a BST to a given range. Time Complexity: $O(n)$.