📂 Python Programming 1st Semester

This repository contains Python lab programs from my BSc. Data Science and Artificial Intelligence coursework. The programs are structured based on five different units, covering fundamental to advanced Python concepts.
This collection provides hands-on experience in control flow, data structures, functions, and modular programming,
gradually progressing towards advanced topics like list comprehensions, lambda functions, and data manipulation using NumPy & Pandas.

Each unit is designed to reinforce algorithmic thinking, improve problem-solving skills,
and enhance code efficiency through best practices.

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📝 Topics Covered

🔹 Unit 1: Python Basics

• Core syntax and variable assignments
• Arithmetic operations & precedence
• Conditional statements (if-else, elif)
• Iterative constructs (for, while loops)
• String manipulation & basic functions

🔹 Unit 2: Lists & Tuples

• Lists: Creation, indexing, slicing, modification
• Tuples: Immutable sequences, tuple packing/unpacking
• Built-in methods (append(), remove(), sort(), etc.)
• Searching & sorting algorithms (linear search, bubble sort)

🔹 Unit 3: Sets & Dictionaries

• Set operations: Union, intersection, difference
• Dictionaries: Keys, values, and methods (get(), items(), etc.)
• Dictionary traversal & comprehensions
• Hashing concepts and efficient lookups

🔹 Unit 4: Comprehensions & Functions

• List, set, and dictionary comprehensions
• Lambda functions (map(), filter(), reduce())
• Recursive functions & generator expressions
• Custom modules & importing best practices

🔹 Unit 5: NumPy & Pandas

• NumPy: Creating arrays, indexing, reshaping, vectorized operations
• Mathematical functions (mean(), sum(), std(), etc.)
• Pandas DataFrames: Data loading, indexing, filtering
• Data preprocessing: Handling missing values, applying functions

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🎯 Learning Outcomes

By completing these exercises, you will:
✅ Gain a strong foundation in Python programming fundamentals.
✅ Develop efficient problem-solving and algorithmic thinking skills.
✅ Understand key data structures and their real-world applications.
✅ Learn functional programming and modular code design.
✅ Work with NumPy & Pandas, essential for data science & AI.

🎯 Purpose

✅ Strengthen Python programming skills through structured exercises
✅ Enhance logical reasoning and algorithm development
✅ Provide a solid foundation for data science and AI applications

🚀 Happy Coding!