07 🎒 Data Structures

Data structures are essential for organizing, storing, and managing data efficiently in Python. Python provides built-in data structures such as Lists, Tuples, Dictionaries, and Sets, each suited for different tasks.

This chapter covers their properties, operations, and use cases in Data Science, Machine Learning, and AI applications.

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07.1 📋 Lists (Ordered, Mutable, Indexed)

A list is an ordered, mutable (changeable) collection that allows duplicate values. Lists are widely used for storing and manipulating datasets.

✅ Creating a List

fruits = ["apple", "banana", "cherry"]
numbers = [10, 20, 30, 40]
mixed = [1, "Python", 3.14, True]

🔹 List Operations

fruits.append("mango")  # Add element
fruits.remove("banana") # Remove element
fruits.insert(1, "grape")  # Insert at index
fruits.pop()  # Remove last item
print(fruits)

✅ Use Case: Managing data records, feature lists, training data batches in ML.

🔹 List Slicing

numbers = [1, 2, 3, 4, 5, 6]
print(numbers[1:4])  # Output: [2, 3, 4]

✅ Use Case: Extracting subsets of data in AI and ML models.

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07.2 📌 Tuples (Ordered, Immutable, Indexed)

A tuple is like a list but immutable (cannot be modified). Tuples are used where data should not change.

✅ Creating a Tuple

coordinates = (10.5, 20.3)
colors = ("red", "green", "blue")

🔹 Tuple Operations

print(coordinates[0])  # Access elements
print(len(colors))     # Tuple length

✅ Use Case: Storing constant data like color codes, geographic coordinates.

🔹 Tuple Packing & Unpacking

point = (3, 4)
x, y = point  # Unpacking
print(x, y)   # Output: 3 4

✅ Use Case: Assigning multiple values in one step in AI and data transformations.

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07.3 🗂️ Dictionaries (Key-Value Pairs, Unordered, Mutable)

A dictionary (dict) stores data in key-value pairs, making it ideal for fast lookups.

✅ Creating a Dictionary

student = {"name": "Alice", "age": 21, "grade": "A"}

🔹 Dictionary Operations

print(student["name"])   # Access value
student["age"] = 22      # Modify value
student["city"] = "NY"   # Add new key-value pair
del student["grade"]     # Remove key-value pair

✅ Use Case: Storing JSON-like data, AI model parameters, ML hyperparameters.

🔹 Iterating Over a Dictionary

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

✅ Use Case: Extracting metadata from datasets, handling API responses.

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07.4 🔥 Sets (Unordered, Unique Elements, Fast Lookups)

A set is an unordered collection of unique elements, useful for removing duplicates and fast lookups.

✅ Creating a Set

numbers = {1, 2, 3, 4, 4, 2}  # Duplicates removed automatically

🔹 Set Operations

numbers.add(5)  # Add element
numbers.remove(3)  # Remove element

🔹 Set Operations for AI & ML

A = {1, 2, 3, 4}
B = {3, 4, 5, 6}

print(A.union(B))    # {1, 2, 3, 4, 5, 6}
print(A.intersection(B))  # {3, 4}
print(A.difference(B))    # {1, 2}

✅ Use Case: Removing duplicate values in datasets, comparing feature sets.

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🚀 Summary

Data Structure	Properties	Use Case
List 📋	Ordered, Mutable	Storing datasets, feature lists
Tuple 📌	Ordered, Immutable	Constants, fixed ML configurations
Dictionary 🗂️	Key-Value Pairs, Mutable	Fast lookups, JSON data, ML parameters
Set 🔥	Unordered, Unique Elements	Removing duplicates, comparing data

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🔚 Final Thoughts

Understanding Lists, Tuples, Dictionaries, and Sets is crucial for data processing, feature engineering, and AI applications.

Would you like real-world coding exercises on these topics? 🚀