🚀 DSA-TimeComplexity

📌 Understanding Time Complexity in Data Structures & Algorithms

This repository contains Python implementations of various time complexity examples, helping you understand how different algorithms perform as input size increases.

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📂 Contents

1️⃣ Measuring Execution Time (⚠️ Not Recommended)

• Using time.time() to measure execution time.
• Why it's not reliable due to varying machine specs.

2️⃣ Iterative vs Recursive Algorithms

• Countdown using while loop.
• Factorial function O(n) complexity (Iterative vs Recursive).

3️⃣ String Conversion (Integer to String)

• Understanding how integers are converted to strings manually.

4️⃣ Nested Loops & Pair Generation

• O(n²) complexity: Generating pairs in a list.
• O(n × m) complexity: Comparing two lists element-wise.

5️⃣ Triple Nested Loops

• Understanding how O(n³) complexity arises.

6️⃣ Swapping Elements in a List

• Optimized O(n) approach to reverse a list in-place.

7️⃣ Fibonacci Sequence (Exponential Complexity 🚨)

• O(2ⁿ) complexity: Why recursive Fibonacci is inefficient.

8️⃣ Dynamic Arrays

• Understanding resizing strategy and amortized time complexity.
• How dynamic arrays achieve O(1) average-time complexity for append().
• Implementing a custom dynamic array in Python.

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⏳ Time Complexity Summary

Algorithm	Time Complexity
Iterative Factorial	O(n)
Recursive Factorial	O(n)
Nested Loops (Pairs)	O(n²)
Comparing Two Lists	O(n × m)
Triple Nested Loops	O(n³)
List Reversal	O(n)
Recursive Fibonacci	O(2ⁿ) (Exponential)
Dynamic Array Append	O(1) (Amortized)

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🚀 How to Run the Code?

1. Clone the repository:

   git clone https://github.com/kamalaly611/DSA-TimeCompleixty.git

2. Navigate to the folder:

   cd DynamicArrays.py

3. Run the Python file:

   python filename.py

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🤔 Why is Time Complexity Important?

• Helps optimize algorithms for large inputs.
• Determines scalability of a solution.
• Avoids inefficiencies in real-world applications.

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🔗 Contribute & Learn More

Want to contribute? Feel free to fork, star ⭐, or open an issue!

📩 Author: kamalaly611

Happy Coding! 🚀