Python for Beginners — Part 5: Collections

Part 5 of a beginner-friendly series on learning Python from scratch.

In Part 4, we learned how to make decisions and repeat code with loops. Now we're going to upgrade that power dramatically: instead of working with single values, we'll work with entire collections of data.

This is where Python becomes truly versatile. Collections let you store multiple values in a single variable, organize data logically, and iterate over it with the loops you just learned. Most real programs spend their time managing collections.

Lists — Ordered, Changeable Collections

A list is a container that holds multiple values in a specific order. You can add, remove, or change items after creating the list.

Creating and accessing lists

fruits = ["apple", "banana", "cherry"]
numbers = [1, 2, 3, 4, 5]
mixed = [1, "hello", 3.14, True]

Lists use square brackets and items are separated by commas. Items can be different types.

Access items by their index (position), just like strings:

fruits = ["apple", "banana", "cherry"]
print(fruits[0])    # apple
print(fruits[1])    # banana
print(fruits[-1])   # cherry (last item)

Get the length with len():

print(len(fruits))  # 3

Slicing lists

Just like strings, lists can be sliced:

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
print(numbers[0:3])     # [1, 2, 3]
print(numbers[5:])      # [6, 7, 8, 9, 10]
print(numbers[::2])     # [1, 3, 5, 7, 9] (every 2nd item)

Essential list methods

Lists come with many built-in methods. Here are the most important:

fruits = ["apple", "banana"]

# Add items
fruits.append("cherry")              # Add to the end
fruits.insert(1, "mango")            # Insert at specific position
fruits.extend(["date", "elderberry"]) # Add multiple items

# Remove items
fruits.remove("banana")              # Remove by value
fruits.pop()                         # Remove last item and return it
fruits.pop(0)                        # Remove item at index 0
fruits.clear()                       # Remove all items

# Find and sort
numbers = [3, 1, 4, 1, 5, 9, 2, 6]
print(numbers.count(1))              # 2 (how many 1's)
print(numbers.index(4))              # 2 (position of 4)
numbers.sort()                       # Sort in place: [1, 1, 2, 3, 4, 5, 6, 9]
numbers.reverse()                    # Reverse in place

# Create a copy
numbers_copy = numbers.copy()        # Independent copy

Important: sort() and reverse() change the list itself, not creating a new list. If you want to keep the original:

sorted_numbers = sorted(numbers)     # Returns a new sorted list

Modifying list items

Lists are mutable (changeable):

fruits = ["apple", "banana", "cherry"]
fruits[1] = "blueberry"              # Change the second item
print(fruits)                        # ["apple", "blueberry", "cherry"]

Iterating over lists

Combine lists with the for loops from Part 4:

fruits = ["apple", "banana", "cherry"]

for fruit in fruits:
    print(fruit)

Output:

apple
banana
cherry

List comprehension — concise list creation

List comprehension is a compact way to create a new list by transforming or filtering an existing one:

# Create a list of squares
numbers = [1, 2, 3, 4, 5]
squares = [x**2 for x in numbers]
print(squares)  # [1, 4, 9, 16, 25]

# Filter: only even numbers
evens = [x for x in numbers if x % 2 == 0]
print(evens)    # [2, 4]

# Transform and filter
result = [x * 2 for x in numbers if x > 2]
print(result)   # [6, 8, 10]

Read it as: "Create a list of [transformation] for [each item] in [source list] if [condition]"

List comprehensions are faster and more Pythonic than building lists with loops. They're common in real code.

Tuples — Ordered, Unchangeable Collections

A tuple is like a list, but immutable (unchangeable). Once created, you can't add, remove, or modify items.

Creating tuples

colors = ("red", "green", "blue")
single_item = ("hello",)  # Note the comma — without it, it's just a string
empty = ()

Use parentheses (round brackets). The comma is crucial for single-item tuples.

Accessing and iterating

colors = ("red", "green", "blue")
print(colors[0])      # red
print(colors[-1])     # blue
print(len(colors))    # 3

for color in colors:
    print(color)

Unpacking tuples

Tuples shine when you want to assign multiple values at once:

x, y = (1, 2)
print(x)  # 1
print(y)  # 2

# The parentheses are optional
name, age, city = "Ramesh", 25, "Chennai"
print(name, age, city)  # Ramesh 25 Chennai

# Swap variables easily
a, b = 1, 2
a, b = b, a  # Now a=2, b=1

Why use tuples?

Speed: Slightly faster than lists (minor difference)
Safety: Immutable, can't accidentally modify
Keys in dictionaries: Tuples can be dict keys; lists can't
Function returns: Functions often return tuples of multiple values

def get_user():
    return ("Ramesh", 25, "Chennai")

name, age, city = get_user()

Sets — Unique, Unordered Collections

A set is a collection of unique values with no specific order. Duplicates are automatically removed.

Creating sets

numbers = {1, 2, 3, 4, 5}
unique_letters = {"a", "b", "c", "a"}  # 'a' appears once
print(unique_letters)                   # {'a', 'b', 'c'} (order may vary)

empty_set = set()  # Note: {} creates an empty dict, not a set

Use curly braces. Sets have no indexing — you can't access the first item like you can with lists.

Common set operations

a = {1, 2, 3}
b = {3, 4, 5}

# Union — all items from both sets
print(a | b)        # {1, 2, 3, 4, 5}
print(a.union(b))   # {1, 2, 3, 4, 5}

# Intersection — items in both sets
print(a & b)        # {3}
print(a.intersection(b))  # {3}

# Difference — items in a but not b
print(a - b)        # {1, 2}
print(a.difference(b))    # {1, 2}

# Check membership
print(3 in a)       # True
print(4 in a)       # False

Set methods

numbers = {1, 2, 3}
numbers.add(4)          # Add one item
numbers.update([5, 6])  # Add multiple items
numbers.remove(2)       # Remove item (error if not found)
numbers.discard(10)     # Remove item (no error if not found)
numbers.clear()         # Remove all items

Use cases for sets

Remove duplicates: unique = set([1, 2, 2, 3, 3, 3]) → {1, 2, 3}
Check membership: Sets are faster than lists for in checks
Mathematical operations: Union, intersection, difference

Dictionaries — Key-Value Pairs

A dictionary stores pairs of keys and values. You access values using their keys, not positions.

Creating dictionaries

person = {
    "name": "Ramesh",
    "age": 25,
    "city": "Chennai"
}

scores = {"math": 95, "science": 87, "history": 92}
empty = {}

Use curly braces with key: value pairs. Keys are usually strings; values can be any type.

Accessing and modifying

person = {"name": "Ramesh", "age": 25}

print(person["name"])       # Ramesh
print(person["age"])        # 25

# Modify existing key
person["age"] = 26

# Add new key
person["city"] = "Chennai"

# Try to access non-existent key (safer way)
person.get("phone")         # None (no error)
person.get("phone", "N/A")  # N/A (default value)

Dictionary methods

person = {"name": "Ramesh", "age": 25, "city": "Chennai"}

# Get all keys, values, or pairs
print(person.keys())        # dict_keys(['name', 'age', 'city'])
print(person.values())      # dict_values(['Ramesh', 25, 'Chennai'])
print(person.items())       # dict_items([('name', 'Ramesh'), ('age', 25), ...])

# Iterate
for key in person:
    print(f"{key}: {person[key]}")

# Iterate over pairs
for key, value in person.items():
    print(f"{key}: {value}")

# Remove items
person.pop("age")           # Remove and return value
person.popitem()            # Remove last pair
person.clear()              # Remove all

# Update from another dict
person.update({"phone": "555-1234", "age": 26})

Nested dictionaries

Dictionaries can contain other dictionaries (or lists, or tuples):

team = {
    "player1": {"name": "Ramesh", "score": 95},
    "player2": {"name": "Priya", "score": 87},
    "player3": {"name": "Arun", "score": 92}
}

print(team["player1"]["name"])  # Ramesh
print(team["player2"]["score"]) # 87

Choosing the Right Collection

Collection	Ordered	Changeable	Duplicates	Use Case
List	✓	✓	✓	Store sequence of items
Tuple	✓	✗	✓	Immutable sequences, dict keys
Set	✗	✓	✗	Unique values, membership checks
Dict	✓ (Python 3.7+)	✓	✓ (values)	Store labeled data, key-value lookup

Practical Examples

Example 1: Managing a to-do list

todos = []

# Add tasks
todos.append("Buy groceries")
todos.append("Finish project")
todos.append("Call mom")

# Mark as done (remove)
todos.remove("Buy groceries")

# Show remaining
for i, task in enumerate(todos):
    print(f"{i+1}. {task}")