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

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

Strings are immutable sequences of characters. Common operations:

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

Numbers

python
# 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

python
is_valid = True
print(is_valid and False)  # False

3.2 Collections

Lists

Mutable, ordered, allow duplicates.

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

Tuples

Immutable, ordered.

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

Sets

Unordered, unique items.

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

Dictionaries

Key-value mappings.

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

3.3 Iterators and Comprehensions

Iterators

Any object that implements __iter__ and __next__.

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

List Comprehensions

Concise way to build lists.

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

Set & Dict Comprehensions

python
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
1"""A simple Contact Book project demonstrating Python data handling."""
2
3def add_contact(contacts: dict, name: str, phone: str, email: str) -> None:
4    """Add a new contact to the dictionary."""
5    contacts[name] = {"phone": phone, "email": email}
6
7
8def search_contact(contacts: dict, name: str) -> dict | None:
9    """Search for a contact by name."""
10    return contacts.get(name)
11
12
13def list_contacts(contacts: dict) -> list[str]:
14    """Return a formatted list of all contacts."""
15    return [f"{name}: {info['phone']} / {info['email']}" for name, info in contacts.items()]
16
17
18if __name__ == "__main__":
19    contacts: dict[str, dict[str, str]] = {}
20
21    add_contact(contacts, "Alice", "123-456", "alice@example.com")
22    add_contact(contacts, "Bob", "555-555", "bob@example.com")
23
24    print("All contacts:")
25    for line in list_contacts(contacts):
26        print(" ", line)
27
28    print("\nSearch result:")
29    result = search_contact(contacts, "Alice")
30    print(result if result else "Not found")

Output

bash
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.