Chapter 3: Data Handling in Python

In this chapter, we explore Python’s core data types and collections. Understanding how Python represents and manipulates data is essential before moving into more advanced programming. We’ll cover:

• Primitive data types: strings, numbers, booleans
• Built-in collections: lists, tuples, sets, dictionaries
• Iterators and comprehensions for concise, efficient data processing
• A practical project: Contact Book, applying everything together

3.1 Primitive Data Types

Strings

name = "Walid"
greeting = f"Hello, {name}!"
print(greeting)  # Hello, Walid

Strings are immutable sequences of characters. Common operations:

s = "Python"
print(len(s))        # 6
print(s.upper())     # "PYTHON"
print(s[0:3])        # "Pyt"

Numbers

# Integers
x = 42  
# Floats
y = 3.14  
# Complex numbers
z = 2 + 3j

Python supports arbitrary-precision integers, so no overflow like in C or Java.

Booleans

is_valid = True
print(is_valid and False)  # False

3.2 Collections

Lists

Mutable, ordered, allow duplicates.

fruits = ["apple", "banana", "cherry"]
fruits.append("orange")
print(fruits)  # ['apple', 'banana', 'cherry', 'orange']

Tuples

Immutable, ordered.

point = (10, 20)
x, y = point  # unpacking

Sets

Unordered, unique items.

nums = {1, 2, 3, 3}
print(nums)  # {1, 2, 3}

Dictionaries

Key-value mappings.

person = {"name": "Alice", "age": 30}
print(person["name"])  # Alice

3.3 Iterators and Comprehensions

Iterators

Any object that implements __iter__ and __next__.

nums = [1, 2, 3]
it = iter(nums)
print(next(it))  # 1
print(next(it))  # 2

List Comprehensions

Concise way to build lists.

squares = [x**2 for x in range(5)]
print(squares)  # [0, 1, 4, 9, 16]

Set & Dict Comprehensions

unique_lengths = {len(w) for w in ["hi", "hello", "hi"]}
print(unique_lengths)  # {2, 5}

word_map = {w: len(w) for w in ["hi", "hello"]}
print(word_map)  # {'hi': 2, 'hello': 5}

3.4 Project: Contact Book

We’ll build a simple CLI-based Contact Book that demonstrates strings, dicts, lists, and comprehensions.

contact_book.py

"""A simple Contact Book project demonstrating Python data handling."""

def add_contact(contacts: dict, name: str, phone: str, email: str) -> None:
    """Add a new contact to the dictionary."""
    contacts[name] = {"phone": phone, "email": email}


def search_contact(contacts: dict, name: str) -> dict | None:
    """Search for a contact by name."""
    return contacts.get(name)


def list_contacts(contacts: dict) -> list[str]:
    """Return a formatted list of all contacts."""
    return [f"{name}: {info['phone']} / {info['email']}" for name, info in contacts.items()]


if __name__ == "__main__":
    contacts: dict[str, dict[str, str]] = {}

    add_contact(contacts, "Alice", "123-456", "alice@example.com")
    add_contact(contacts, "Bob", "555-555", "bob@example.com")

    print("All contacts:")
    for line in list_contacts(contacts):
        print(" ", line)

    print("\nSearch result:")
    result = search_contact(contacts, "Alice")
    print(result if result else "Not found")

Output

All contacts:
  Alice: 123-456 / alice@example.com
  Bob: 555-555 / bob@example.com

Search result:
{'phone': '123-456', 'email': 'alice@example.com'}

Summary

• Strings, numbers, and booleans form the primitive building blocks of Python programs.
• Lists, tuples, sets, and dictionaries are core data structures.
• Iterators and comprehensions make code concise and expressive.
• Practical application: the Contact Book combines multiple data structures for a real-world use case.