Readme.md

📘 Day 4 – Data Structures in Python

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🔰 Topic Overview

Today's class focused on one of the most crucial foundations in Python – Data Structures. Understanding how to store, access, and manipulate data using built-in structures is key to writing efficient and readable code.

We covered:

• What are Data Structures?

• Why we need them in Python

• Four core built-in types:

  1. Lists
  2. Tuples
  3. Dictionaries
  4. Sets

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🔗 File Link

🔗 Source Code File: data_structures.py

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🔸 1. Lists – []

• Lists are ordered, mutable, and allow duplicates.
• They can store mixed data types.

✅ Example:

grocery_list = ["Banana", "Milk", "Chicken"]
for item in grocery_list:
    print(item)

✅ Real World Example:

Top_10_populated_country = ["India", "China", "United States", "Indonesia", "Pakistan"]
Top_10_populated_country.append("Japan")  # Add
Top_10_populated_country.remove("Mexico")  # Remove

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🔸 2. Tuples – ()

• Tuples are ordered, immutable, and allow duplicates.
• Often used for fixed collections like RGB codes, coordinates, etc.

✅ Examples:

colour_sets = (
    ("Sky Blue", "#87CEEB"),
    ("Crimson", "#DC143C"),
    ("Neon Green", "#39FF14")
)
for colour in colour_sets:
    print(f"Colour: {colour[0]}, hexcode: {colour[1]}")

✅ Prime Minister Records:

indian_prime_ministers = [
    ("Jawaharlal Nehru", 1947, 1964),
    ("Narendra Modi", 2014, 2024)
]

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🔸 3. Dictionaries – {}

• Dictionaries store data as key-value pairs.
• They are unordered, mutable, and don’t allow duplicate keys.

✅ Examples:

student = {
    "name": "Arjun",
    "age": 21,
    "course": "AI & ML"
}

✅ Nested Movie Reviews:

movies = {
    "superman 2025": {
        "Director": "James Gunn",
        "Lead Actor": "David Corenswet"
    }
}

✅ Looping & Updating:

for key, value in student.items():
    print(f"{key} : {value}")

student["grade"] = "A+"  # Add

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🔸 4. Sets – {}

• Sets are unordered, mutable, and do not allow duplicates.
• Ideal for storing unique values and doing mathematical operations.

✅ Examples:

fruits = {"apple", "banana", "mango"}
fruits.add("grape")
fruits.remove("banana")

✅ Use Case:

numbers = set([1, 2, 2, 3, 4, 4, 5])
print(numbers)  # duplicates removed

✅ Set Operations:

a = {1, 2, 3}
b = {3, 4, 5}
print(a.union(b))
print(a.intersection(b))

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📝 Summary

✔️ Covered all 4 built-in data structures

✔️ Hands-on code examples for each

✔️ Set operations and dictionary nesting

✔️ Real-world mini tasks explored for each type

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