ppant · ppant · Jun 28, 2026
diff --git a/Arrays/Anagram_Check_manual_Sol.py → Arrays/AnagramCheckManualSol.py b/Arrays/Anagram_Check_manual_Sol.py → Arrays/AnagramCheckManualSol.py
diff --git a/Arrays/Anagram_Check_Sorted_Sol.py → Arrays/AnagramCheckSortedSol.py b/Arrays/Anagram_Check_Sorted_Sol.py → Arrays/AnagramCheckSortedSol.py
diff --git a/...yFindTheMissingElement_brute_force_sol.py → ...rrayFindTheMissingElementBruteForceSol.py b/...yFindTheMissingElement_brute_force_sol.py → ...rrayFindTheMissingElementBruteForceSol.py
diff --git a/...ayFindTheMissingElement_hash_table_sol.py → ...ArrayFindTheMissingElementHashTableSol.py b/...ayFindTheMissingElement_hash_table_sol.py → ...ArrayFindTheMissingElementHashTableSol.py
diff --git a/...issingElement_takingSumandSubtract_sol.py → Arrays/ArrayFindTheMissingElementSumSol.py b/...issingElement_takingSumandSubtract_sol.py → Arrays/ArrayFindTheMissingElementSumSol.py
diff --git a/Arrays/ArrayFindTheMissingElement_XOR_sol.py → Arrays/ArrayFindTheMissingElementXORSol.py b/Arrays/ArrayFindTheMissingElement_XOR_sol.py → Arrays/ArrayFindTheMissingElementXORSol.py
diff --git a/Arrays/README.md b/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.
-- [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.
+- [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: $O(n)$, Space Complexity: $O(1)$.
+- [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. Time Complexity: $O(n)$.
diff --git a/deque/DequeImple.py → Deque/DequeImple.py b/deque/DequeImple.py → Deque/DequeImple.py
diff --git a/deque/README.md → Deque/README.md b/deque/README.md → Deque/README.md
@@ -4,4 +4,4 @@ This directory contains Python implementations of the Deque (Double-Ended Queue)
 
 ## Contents
 
-- [Deque Implementation](DequeImple.py): Basic implementation of a Deque using a Python list. Includes operations like `addFront`, `addRear`, `removeFront`, `removeRear`, `isEmpty`, and `size`.
+- [Deque Implementation](DequeImple.py): Basic implementation of a Deque using a Python list. Includes operations like `addFront`, `addRear`, `removeFront`, and `removeRear`. Time Complexity: $O(1)$ for front operations, $O(n)$ for rear operations (due to list shifting).
diff --git a/Error-debug/ErrorExceptions.py → ErrorHandling/ErrorExceptions.py b/Error-debug/ErrorExceptions.py → ErrorHandling/ErrorExceptions.py
diff --git a/Error-debug/README.md → ErrorHandling/README.md b/Error-debug/README.md → ErrorHandling/README.md
@@ -1,7 +1,7 @@
-# Error and Debugging
+# Error and Handling
 
 This directory contains examples of error handling and debugging techniques in Python.
 
 ## Contents
 
-- [Error and Exceptions](ErrorExceptions.py): Demonstrates the use of `try`, `except`, `else`, and `finally` blocks for robust error handling, specifically validating integer user input.
+- [Error and Exceptions](ErrorExceptions.py): Demonstrates the use of `try`, `except`, `else`, and `finally` blocks for robust error handling.
diff --git a/LinkedLists/README.md b/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.
-- [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.
+- [Singly Linked List Implementation](SinglyLinkedListImple.py): Basic implementation of a singly linked list node and basic linkage. Time Complexity: $O(1)$ for linkage.
+- [Doubly Linked List Implementation](DoublyLinkedListImple.py): Basic implementation of a doubly linked list node with `prev` and `next` pointers. Time Complexity: $O(1)$ for linkage.
+- [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. Time Complexity: $O(n)$.
+- [Nth to Last Node](LinkedListNthToLastNode.py): Finds the $n$-th to last node in a singly linked list using two pointers. Time Complexity: $O(n)$.
diff --git a/LinkedLists/SinglyLinkedListCycleCheckImple.py b/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
@@ -31,6 +31,7 @@ def cycle_check(node):
             if pt2 == pt1:
                 return True
         return False
+
 # Test
 # Create a Linked List 
 a = LinkedListNode(1)
@@ -39,19 +40,14 @@ def cycle_check(node):
 # Create a cycle
 a.nextnode = b
 b.nextnode = c
-# This is a cycle -- to test non-cycle scnerio comment this line
 c.nextnode = a
 
-print (a.value)
-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())
+print(f"Node A value: {a.value}")
+print(f"Node B value: {b.value}")
+print(f"Node C value: {c.value}")
 
+print(f"Cycle detected (should be True): {a.cycle_check()}")
 
+# Breaking the cycle for testing
+c.nextnode = None
+print(f"Cycle detected after breaking (should be False): {a.cycle_check()}")
diff --git a/LinkedLists/SingleLinkedListImple.py → LinkedLists/SinglyLinkedListImple.py b/LinkedLists/SingleLinkedListImple.py → LinkedLists/SinglyLinkedListImple.py
diff --git a/README.md b/README.md
@@ -35,23 +35,23 @@ See [CONTRIBUTING.md](CONTRIBUTING.md) for more details.
 
 ## 📖 Table of Contents
 
-- [Getting Started](#getting-started)
-- [Project Structure](#project-structure)
-- [Data Structures](#data-structures)
-  - [Arrays](#arrays)
-  - [Linked Lists](#linked-lists)
-  - [Stacks](#stacks)
-  - [Queues](#queues)
-  - [Deque](#deque)
-  - [Trees](#trees)
-- [Algorithms](#algorithms)
-  - [Sorting](#sorting)
-  - [Recursion & Dynamic Programming](#recursion--dynamic-programming)
-  - [Graph Algorithms](#graph-algorithms)
-- [Error Handling & Debugging](#error-handling--debugging)
-- [Usage](#usage)
-- [Quick Reference](#quick-reference)
-- [License](#license)
+- [🚀 Getting Started](#getting-started)
+- [📁 Project Structure](#project-structure)
+- [📊 Data Structures](#data-structures)
+  - [Arrays 🔤](#arrays)
+  - [Linked Lists 🔗](#linked-lists)
+  - [Stacks 📚](#stacks)
+  - [Queues 📦](#queues)
+  - [Deque 🔄](#deque)
+  - [Trees 🌳](#trees)
+- [⚡ Algorithms](#algorithms)
+  - [Sorting 📊](#sorting)
+  - [Recursion & Dynamic Programming 🔀](#recursion--dynamic-programming)
+  - [Graph Algorithms 🗺️](#graph-algorithms)
+- [⚠️ Error Handling & Debugging](#error-handling--debugging)
+- [📖 Usage](#usage)
+- [📊 Quick Reference](#quick-reference)
+- [📄 License](#license)
 
 ---
 
@@ -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) - $O(n^2)$ worst case
 - [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.
 
 ---
 
@@ -214,8 +214,9 @@ New to DSA? Follow this recommended order:
 
 ## 🔮 Roadmap
 
+- [x] Correct naming conventions (PascalCase)
+- [x] Include complexity analysis for each solution
 - [ ] Add more graph algorithms (Dijkstra, Bellman-Ford)
-- [ ] Include complexity analysis for each solution
 - [ ] Add interactive examples/visualizations
 - [ ] Create a difficulty level classification
 - [ ] Add more test cases

diff --git a/Recursion/FibonacciSeqDynamic.py b/Recursion/FibonacciSeqDynamic.py
@@ -2,12 +2,11 @@
 # memoization + recursion = dynamic programming
 # Author: Pradeep K. Pant, ppant@cpan.org
 
-# Instantiate Cache information
-n = 10
-cache = [None] * (n + 1)
-
 # Implement fibonacci_dynamic
-def fibonacci_dynamic(n):
+def fibonacci_dynamic(n, cache=None):
+    if cache is None:
+        cache = [None] * (n + 1)
+
     # Base Case
     if n == 0 or n == 1:
         return n
@@ -17,7 +16,7 @@ def fibonacci_dynamic(n):
         return cache[n]
 
     # Recursive call to and keep setting cache
-    cache[n] = fibonacci_dynamic(n - 1) + fibonacci_dynamic(n - 2)
+    cache[n] = fibonacci_dynamic(n - 1, cache) + fibonacci_dynamic(n - 2, cache)
     return cache[n]
 
 # Test

diff --git a/Sorting/README.md b/Sorting/README.md
@@ -4,9 +4,9 @@ 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.
-- [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.
+- [Bubble Sort](BubbleSortImple.py): Implementation of Bubble Sort. Time Complexity: $O(n^2)$.
+- [Selection Sort](SelectionSortImple.py): Implementation of Selection Sort, 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. Time Complexity: $O(n \log n)$.
+- [Quick Sort](QuickSortImple.py): Implementation of Quick Sort (partition exchange sort). Time Complexity: $O(n \log n)$ average.
diff --git a/Stacks/BalanceParenthlessCheckImple.py → Stacks/BalanceParenthesesCheckImple.py b/Stacks/BalanceParenthlessCheckImple.py → Stacks/BalanceParenthesesCheckImple.py
diff --git a/Stacks/README.md b/Stacks/README.md
@@ -4,5 +4,5 @@ This directory contains Python implementations of the Stack data structure and i
 
 ## 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 LIFO (Last-In-First-Out) stack using a Python list. Time Complexity: $O(1)$ for push/pop/peek.
+- [Balanced Parentheses Check](BalanceParenthesesCheckImple.py): Uses a stack to check if a string of opening and closing parentheses (round, square, and curly) is balanced. Time Complexity: $O(n)$.
diff --git a/Trees/buildTreeTest.py → Trees/BuildTreeTest.py b/Trees/buildTreeTest.py → Trees/BuildTreeTest.py
diff --git a/Trees/README.md b/Trees/README.md
@@ -5,19 +5,19 @@ This directory contains Python implementations of various tree-based data struct
 ## 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 $[min, max]$.
+- [Binary Search Tree Implementation](BinarySearchTreesImple.py): A comprehensive implementation of a BST with `TreeNode` and `BinarySearchTree` classes. Time Complexity: $O(\log n)$ average, $O(n)$ worst case.
+- [Validate BST (Solution 1)](BinarySearchTreeCheckImpleSol1.py): Validates a BST by performing an in-order traversal. Time Complexity: $O(n)$.
+- [Validate BST (Solution 2)](BinarySearchTreeCheckImpleSol2.py): Validates a BST by keeping track of the minimum and maximum allowable values. Time Complexity: $O(n)$.
+- [Trim a BST](TrimBinarySearchTreeImple.py): Trims a BST so that all node values fall within a specified range $[min, max]$. Time Complexity: $O(n)$.
 
 ### 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.
+- [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: $O(\log n)$.
 
 ### Heaps
-- [Binary Heap Implementation](BinaryHeapImple.py): Implements a min-heap using a recursive approach, including `insert`, `delMin`, and `buildHeap`.
+- [Binary Heap Implementation](BinaryHeapImple.py): Implements a min-heap using a recursive approach. Time Complexity: $O(\log n)$ for insert/delete, $O(n)$ for 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.
+- [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. Time Complexity: $O(n)$.
Original file line number	Diff line number	Diff line change
Expand Up		@@ -4,4 +4,4 @@ This directory contains Python implementations of the Deque (Double-Ended Queue)

		## Contents

		- [Deque Implementation](DequeImple.py): Basic implementation of a Deque using a Python list. Includes operations like `addFront`, `addRear`, `removeFront`, `removeRear`, `isEmpty`, and `size`.
		- [Deque Implementation](DequeImple.py): Basic implementation of a Deque using a Python list. Includes operations like `addFront`, `addRear`, `removeFront`, and `removeRear`. Time Complexity: $O(1)$ for front operations, $O(n)$ for rear operations (due to list shifting).