Picture this: You're heading to the grocery store. You grab a pen and scribble on a piece of paper — milk, eggs, bread, apples, cheese. As you walk through the aisles, you cross items off. Near the checkout, you remember you need butter, so you add it at the bottom.

That piece of paper is a list. It holds multiple items, in order, and you can add to it, remove from it, and check what's in it at any time.

Python lists work exactly the same way — except they live in your computer's memory instead of your pocket.

Why does this matter? Without lists, every piece of related data needs its own variable. Imagine writing score1 = 85, score2 = 92, score3 = 78... for 30 students. Lists let you group related data together under one name and work with it efficiently.

What Is a List?

A list is a Python data structure that stores multiple items in a single variable, in a specific order. Lists are:

Ordered — items stay in the order you put them
Mutable — you can change, add, or remove items after creation
Indexed — each item has a position number
Mixed-type — one list can hold strings, numbers, and more

Creating a List

# An empty list
shopping = []

# A list with items
fruits = ["apple", "banana", "cherry"]

# A list of numbers
scores = [85, 92, 78, 95, 88]

# A mixed list
person = ["Alice", 25, True, 5.6]

Line by line:

[] — square brackets define a list
Items are separated by commas
Strings use quotes; numbers do not

Accessing Items: Indexing

Every item in a list has an index — a position number. Python starts counting at 0, not 1. This trips up beginners, but you'll quickly get used to it.

Think of it like apartment numbers in a building where the ground floor is floor 0.

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

print(fruits[0])   # apple
print(fruits[2])   # cherry
print(fruits[-1])  # elderberry (last item)
print(fruits[-2])  # date (second to last)

Index Reference Table

Item	Positive Index	Negative Index
`"apple"`	0	-5
`"banana"`	1	-4
`"cherry"`	2	-3
`"date"`	3	-2
`"elderberry"`	4	-1

Tip: Negative indexing counts from the end. -1 is always the last item, which is handy when you don't know how long the list is.

Common List Operations

Adding Items

cart = ["bread", "milk"]

# append() — adds to the END
cart.append("eggs")
print(cart)  # ['bread', 'milk', 'eggs']

# insert(position, item) — adds at a specific position
cart.insert(1, "butter")
print(cart)  # ['bread', 'butter', 'milk', 'eggs']

append(item) — always adds to the end. Fast and simple. insert(index, item) — inserts before the given index position.

Removing Items

cart = ["bread", "butter", "milk", "eggs", "milk"]

# remove() — removes FIRST occurrence of a value
cart.remove("milk")
print(cart)  # ['bread', 'butter', 'eggs', 'milk']

# pop() — removes by index (default: last item), RETURNS the removed item
removed = cart.pop()
print(removed)  # 'milk'
print(cart)     # ['bread', 'butter', 'eggs']

# pop with index
removed = cart.pop(0)
print(removed)  # 'bread'
print(cart)     # ['butter', 'eggs']

Useful Built-in Functions

numbers = [3, 1, 4, 1, 5, 9, 2, 6]

print(len(numbers))   # 8  — count of items
print(sum(numbers))   # 31 — total of all numbers
print(max(numbers))   # 9  — largest item
print(min(numbers))   # 1  — smallest item

Sorting and Reversing

scores = [85, 42, 93, 67, 78]

scores.sort()              # sorts in-place, ascending
print(scores)              # [42, 67, 78, 85, 93]

scores.sort(reverse=True)  # sorts descending
print(scores)              # [93, 85, 78, 67, 42]

scores.reverse()           # flips the order in-place
print(scores)              # [42, 67, 78, 85, 93]

Important: .sort() and .reverse() modify the original list. They do not return a new one.

Checking Membership with `in`

colors = ["red", "green", "blue"]

print("red" in colors)     # True
print("yellow" in colors)  # False
print("green" not in colors)  # False

This is the Python way of asking: "Is this item somewhere in my list?"

Slicing: Grabbing a Portion

Slicing lets you extract a sub-list from a list. The syntax is:

list[start:end:step]

start — index to begin (included)
end — index to stop (NOT included)
step — how many to skip (optional, default is 1)

months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun",
          "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]

print(months[0:3])    # ['Jan', 'Feb', 'Mar']
print(months[6:9])    # ['Jul', 'Aug', 'Sep']
print(months[:3])     # ['Jan', 'Feb', 'Mar'] — start defaults to 0
print(months[9:])     # ['Oct', 'Nov', 'Dec'] — end defaults to end of list
print(months[::2])    # Every other month: ['Jan', 'Mar', 'May', ...]
print(months[::-1])   # Reversed: ['Dec', 'Nov', 'Oct', ...]

Lists Can Hold Different Types

Unlike some languages, Python lists can mix types freely:

profile = ["Alice", 28, 5.7, True, ["Python", "SQL"]]

print(profile[0])   # 'Alice'   — string
print(profile[1])   # 28        — integer
print(profile[4])   # ['Python', 'SQL'] — a list inside a list!

Iterating Over a List

Use a for loop to visit every item in a list:

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

for fruit in fruits:
    print(f"I like {fruit}")

Output:

I like apple
I like banana
I like cherry

To also get the index, use enumerate():

for index, fruit in enumerate(fruits):
    print(f"{index}: {fruit}")

Output:

0: apple
1: banana
2: cherry

List Comprehension: One-Line Power

List comprehension is a compact way to create a new list based on an existing one. It reads almost like plain English.

# Traditional way
squares = []
for n in range(1, 6):
    squares.append(n ** 2)

# List comprehension — same result, one line
squares = [n ** 2 for n in range(1, 6)]
print(squares)  # [1, 4, 9, 16, 25]

# With a condition
even_squares = [n ** 2 for n in range(1, 11) if n % 2 == 0]
print(even_squares)  # [4, 16, 36, 64, 100]

The pattern is: [expression for item in iterable if condition]

Real Project: Student Score Tracker

Let's build something practical. A teacher wants to enter student scores one by one and get a summary report at the end.

scores = []

while True:
    entry = input("Enter score (or 'done' to finish): ")
    if entry.lower() == 'done':
        break
    scores.append(float(entry))

if scores:
    print(f"\n--- Score Report ---")
    print(f"Scores: {scores}")
    print(f"Count: {len(scores)}")
    print(f"Average: {sum(scores)/len(scores):.1f}")
    print(f"Highest: {max(scores)}")
    print(f"Lowest: {min(scores)}")
    scores.sort()
    print(f"Sorted: {scores}")

How it works:

scores = [] — starts with an empty list
while True: — loops indefinitely until we break out
entry.lower() == 'done' — case-insensitive check for the exit word
scores.append(float(entry)) — converts the string input to a float and adds to the list
if scores: — only runs the report if at least one score was entered
sum(scores)/len(scores):.1f — calculates average, formatted to 1 decimal place

Sample Run:

Enter score (or 'done' to finish): 85
Enter score (or 'done' to finish): 92
Enter score (or 'done' to finish): 78
Enter score (or 'done' to finish): 95
Enter score (or 'done' to finish): 88
Enter score (or 'done' to finish): done

--- Score Report ---
Scores: [85.0, 92.0, 78.0, 95.0, 88.0]
Count: 5
Average: 87.6
Highest: 95.0
Lowest: 78.0
Sorted: [78.0, 85.0, 88.0, 92.0, 95.0]

Quick Reference

Operation	Syntax	Example
Create list	`[item1, item2]`	`nums = [1, 2, 3]`
Access item	`list[index]`	`nums[0]` → `1`
Add to end	`list.append(item)`	`nums.append(4)`
Insert at position	`list.insert(i, item)`	`nums.insert(0, 0)`
Remove by value	`list.remove(item)`	`nums.remove(2)`
Remove by index	`list.pop(i)`	`nums.pop()`
Length	`len(list)`	`len(nums)` → `3`
Sort	`list.sort()`	`nums.sort()`
Slice	`list[start:end]`	`nums[0:2]`
Check membership	`item in list`	`1 in nums` → `True`

Key Takeaways

Lists store ordered, mutable collections of items
Indexing starts at 0 — the first item is always index 0
Negative indices count from the end of the list
Use append() to add, remove() or pop() to delete
Slicing extracts sublists with list[start:end:step]
List comprehension creates new lists in one elegant line

In the next lesson, we'll explore dictionaries — a different kind of collection where data has labels instead of position numbers.

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28 minLesson 13 of 19

Course Contents(19 lessons)

▾

Chapter 1: What is Programming?

How Computers Think and Follow Instructions18 min

Why Python? Setting Up Your Environment20 min

Your First Python Program — Hello, World!15 min

Chapter 2: Variables and Data Types

Variables: Giving Names to Information22 min

Numbers, Text, and True/False (Data Types)25 min

Getting Input from Users and Type Conversion20 min

Chapter 3: Making Decisions (Control Flow)

If-Else: Teaching Your Program to Choose28 min

Loops: Making Your Computer Do Repetitive Work30 min

Nested Conditions and Loop Control (break/continue)22 min

Chapter 4: Functions — Write Once, Use Forever

What Are Functions and Why They Matter25 min

Parameters, Arguments, and Return Values28 min

Scope: Where Variables Live and Die20 min

Chapter 5: Working with Collections

Lists: Storing Multiple Items in One Place28 min

Dictionaries: Key-Value Storage Explained25 min

String Methods: Manipulating Text Like a Pro22 min

Chapter 6: Object-Oriented Programming Basics

Classes and Objects: Real World in Code35 min

Methods and Attributes: What Objects Know and Do30 min

Chapter 7: Debugging and Final Project

Reading Errors: Python Error Messages Decoded25 min

Final Project: Build a Student Grade Calculator45 min

Chapter 5: Working with Collections

Lists: Storing Multiple Items in One Place

The Shopping List Moment

That piece of paper is a list. It holds multiple items, in order, and you can add to it, remove from it, and check what's in it at any time.

Python lists work exactly the same way — except they live in your computer's memory instead of your pocket.

Why does this matter? Without lists, every piece of related data needs its own variable. Imagine writing score1 = 85, score2 = 92, score3 = 78... for 30 students. Lists let you group related data together under one name and work with it efficiently.

What Is a List?

A list is a Python data structure that stores multiple items in a single variable, in a specific order. Lists are:

Ordered — items stay in the order you put them
Mutable — you can change, add, or remove items after creation
Indexed — each item has a position number
Mixed-type — one list can hold strings, numbers, and more

Creating a List

# An empty list
shopping = []

# A list with items
fruits = ["apple", "banana", "cherry"]

# A list of numbers
scores = [85, 92, 78, 95, 88]

# A mixed list
person = ["Alice", 25, True, 5.6]

Line by line:

[] — square brackets define a list
Items are separated by commas
Strings use quotes; numbers do not

Accessing Items: Indexing

Every item in a list has an index — a position number. Python starts counting at 0, not 1. This trips up beginners, but you'll quickly get used to it.

Think of it like apartment numbers in a building where the ground floor is floor 0.

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

print(fruits[0])   # apple
print(fruits[2])   # cherry
print(fruits[-1])  # elderberry (last item)
print(fruits[-2])  # date (second to last)

Index Reference Table

Item	Positive Index	Negative Index
`"apple"`	0	-5
`"banana"`	1	-4
`"cherry"`	2	-3
`"date"`	3	-2
`"elderberry"`	4	-1

Tip: Negative indexing counts from the end. -1 is always the last item, which is handy when you don't know how long the list is.

Common List Operations

Adding Items

cart = ["bread", "milk"]

# append() — adds to the END
cart.append("eggs")
print(cart)  # ['bread', 'milk', 'eggs']

# insert(position, item) — adds at a specific position
cart.insert(1, "butter")
print(cart)  # ['bread', 'butter', 'milk', 'eggs']

append(item) — always adds to the end. Fast and simple. insert(index, item) — inserts before the given index position.

Removing Items

cart = ["bread", "butter", "milk", "eggs", "milk"]

# remove() — removes FIRST occurrence of a value
cart.remove("milk")
print(cart)  # ['bread', 'butter', 'eggs', 'milk']

# pop() — removes by index (default: last item), RETURNS the removed item
removed = cart.pop()
print(removed)  # 'milk'
print(cart)     # ['bread', 'butter', 'eggs']

# pop with index
removed = cart.pop(0)
print(removed)  # 'bread'
print(cart)     # ['butter', 'eggs']

Useful Built-in Functions

numbers = [3, 1, 4, 1, 5, 9, 2, 6]

print(len(numbers))   # 8  — count of items
print(sum(numbers))   # 31 — total of all numbers
print(max(numbers))   # 9  — largest item
print(min(numbers))   # 1  — smallest item

Sorting and Reversing

scores = [85, 42, 93, 67, 78]

scores.sort()              # sorts in-place, ascending
print(scores)              # [42, 67, 78, 85, 93]

scores.sort(reverse=True)  # sorts descending
print(scores)              # [93, 85, 78, 67, 42]

scores.reverse()           # flips the order in-place
print(scores)              # [42, 67, 78, 85, 93]

Important: .sort() and .reverse() modify the original list. They do not return a new one.

Checking Membership with `in`

colors = ["red", "green", "blue"]

print("red" in colors)     # True
print("yellow" in colors)  # False
print("green" not in colors)  # False

This is the Python way of asking: "Is this item somewhere in my list?"

Slicing: Grabbing a Portion

Slicing lets you extract a sub-list from a list. The syntax is:

list[start:end:step]

start — index to begin (included)
end — index to stop (NOT included)
step — how many to skip (optional, default is 1)

months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun",
          "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]

print(months[0:3])    # ['Jan', 'Feb', 'Mar']
print(months[6:9])    # ['Jul', 'Aug', 'Sep']
print(months[:3])     # ['Jan', 'Feb', 'Mar'] — start defaults to 0
print(months[9:])     # ['Oct', 'Nov', 'Dec'] — end defaults to end of list
print(months[::2])    # Every other month: ['Jan', 'Mar', 'May', ...]
print(months[::-1])   # Reversed: ['Dec', 'Nov', 'Oct', ...]

Lists Can Hold Different Types

Unlike some languages, Python lists can mix types freely:

profile = ["Alice", 28, 5.7, True, ["Python", "SQL"]]

print(profile[0])   # 'Alice'   — string
print(profile[1])   # 28        — integer
print(profile[4])   # ['Python', 'SQL'] — a list inside a list!

Iterating Over a List

Use a for loop to visit every item in a list:

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

for fruit in fruits:
    print(f"I like {fruit}")

Output:

I like apple
I like banana
I like cherry

To also get the index, use enumerate():

for index, fruit in enumerate(fruits):
    print(f"{index}: {fruit}")

Output:

0: apple
1: banana
2: cherry

List Comprehension: One-Line Power

List comprehension is a compact way to create a new list based on an existing one. It reads almost like plain English.

# Traditional way
squares = []
for n in range(1, 6):
    squares.append(n ** 2)

# List comprehension — same result, one line
squares = [n ** 2 for n in range(1, 6)]
print(squares)  # [1, 4, 9, 16, 25]

# With a condition
even_squares = [n ** 2 for n in range(1, 11) if n % 2 == 0]
print(even_squares)  # [4, 16, 36, 64, 100]

The pattern is: [expression for item in iterable if condition]

Real Project: Student Score Tracker

Let's build something practical. A teacher wants to enter student scores one by one and get a summary report at the end.

scores = []

while True:
    entry = input("Enter score (or 'done' to finish): ")
    if entry.lower() == 'done':
        break
    scores.append(float(entry))

if scores:
    print(f"\n--- Score Report ---")
    print(f"Scores: {scores}")
    print(f"Count: {len(scores)}")
    print(f"Average: {sum(scores)/len(scores):.1f}")
    print(f"Highest: {max(scores)}")
    print(f"Lowest: {min(scores)}")
    scores.sort()
    print(f"Sorted: {scores}")

How it works:

scores = [] — starts with an empty list
while True: — loops indefinitely until we break out
entry.lower() == 'done' — case-insensitive check for the exit word
scores.append(float(entry)) — converts the string input to a float and adds to the list
if scores: — only runs the report if at least one score was entered
sum(scores)/len(scores):.1f — calculates average, formatted to 1 decimal place

Sample Run:

Enter score (or 'done' to finish): 85
Enter score (or 'done' to finish): 92
Enter score (or 'done' to finish): 78
Enter score (or 'done' to finish): 95
Enter score (or 'done' to finish): 88
Enter score (or 'done' to finish): done

--- Score Report ---
Scores: [85.0, 92.0, 78.0, 95.0, 88.0]
Count: 5
Average: 87.6
Highest: 95.0
Lowest: 78.0
Sorted: [78.0, 85.0, 88.0, 92.0, 95.0]

Quick Reference

Operation	Syntax	Example
Create list	`[item1, item2]`	`nums = [1, 2, 3]`
Access item	`list[index]`	`nums[0]` → `1`
Add to end	`list.append(item)`	`nums.append(4)`
Insert at position	`list.insert(i, item)`	`nums.insert(0, 0)`
Remove by value	`list.remove(item)`	`nums.remove(2)`
Remove by index	`list.pop(i)`	`nums.pop()`
Length	`len(list)`	`len(nums)` → `3`
Sort	`list.sort()`	`nums.sort()`
Slice	`list[start:end]`	`nums[0:2]`
Check membership	`item in list`	`1 in nums` → `True`

Key Takeaways

Lists store ordered, mutable collections of items
Indexing starts at 0 — the first item is always index 0
Negative indices count from the end of the list
Use append() to add, remove() or pop() to delete
Slicing extracts sublists with list[start:end:step]
List comprehension creates new lists in one elegant line

In the next lesson, we'll explore dictionaries — a different kind of collection where data has labels instead of position numbers.

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📱

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Free cheat sheets, summaries & practice exercises

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Lists: Storing Multiple Items in One Place

Lists: Storing Multiple Items in One Place

The Shopping List Moment

What Is a List?

Creating a List

Accessing Items: Indexing

Index Reference Table

Common List Operations

Adding Items

Removing Items

Useful Built-in Functions

Sorting and Reversing

Checking Membership with in

Slicing: Grabbing a Portion

Lists Can Hold Different Types

Iterating Over a List

List Comprehension: One-Line Power

Real Project: Student Score Tracker

Quick Reference

Key Takeaways

💬 DiscussionPowered by GitHub Discussions

Lists: Storing Multiple Items in One Place

Lists: Storing Multiple Items in One Place

The Shopping List Moment

What Is a List?

Creating a List

Accessing Items: Indexing

Index Reference Table

Common List Operations

Adding Items

Removing Items

Useful Built-in Functions

Sorting and Reversing

Checking Membership with in

Slicing: Grabbing a Portion

Lists Can Hold Different Types

Iterating Over a List

List Comprehension: One-Line Power

Real Project: Student Score Tracker

Quick Reference

Key Takeaways

💬 DiscussionPowered by GitHub Discussions

Checking Membership with `in`

Checking Membership with `in`