Lists

Overview

In the previous lesson, you learned how to control program flow using conditional logic and boolean expressions. So far, you've stored only one value per variable. In this lesson, you'll learn about lists — Python's built-in tool for storing and working with ordered collections of values. Lists are fundamental to data science, where you frequently work with sequences of numbers, names, or categories.

Learning Objective

By the end of this lesson, you will have learned how to:

  • Create and modify lists (append, remove, slicing).
  • Use list methods (sort, reverse) and list comprehensions.

Key terms

List: An ordered, mutable collection of items enclosed in square brackets, where items can be of any data type.

Index: The numeric position of an item in a list, starting at 0 for the first item.

Mutable: Able to be changed after creation. Lists are mutable — you can add, remove, or update items.

append(): A list method that adds a single item to the end of a list.

remove(): A list method that removes the first occurrence of a specified value from a list.

List comprehension: A concise, one-line syntax for creating a new list by applying an expression to each item in an existing list.

Starter Code

This lesson encourages experimentation. Use the included Colaboratory notebook to run the code as you learn, or read the lesson and then freely experiment with the notebook's code.

Introduction

In data science, you rarely work with a single value. You work with datasets — collections of many values. Python's list is the most versatile built-in structure for holding an ordered sequence of items. Whether you're storing exam scores, country names, or stock prices, lists give you a flexible way to group related data and process it efficiently.

Creating Lists

Create a list by placing items inside square brackets [], separated by commas:

# A list of numbers
scores = [85, 92, 78, 90, 88]

# A list of strings
fruits = ["apple", "banana", "cherry"]

# A mixed list (allowed but uncommon in data science)
mixed = [42, "hello", True, 3.14]

# An empty list
empty = []

Check the number of items with len():

print(len(scores))   # Output: 5
print(len(fruits))   # Output: 3

Accessing Items

Indexing

Access individual items using their index inside square brackets. Remember: Python indices start at 0.

fruits = ["apple", "banana", "cherry"]
print(fruits[0])   # Output: apple
print(fruits[1])   # Output: banana
print(fruits[-1])  # Output: cherry  (last item)
print(fruits[-2])  # Output: banana  (second to last)

Slicing

Extract a sub-list using [start:end] — the same syntax as string slicing:

scores = [85, 92, 78, 90, 88, 76, 95]
print(scores[1:4])   # Output: [92, 78, 90]
print(scores[:3])    # Output: [85, 92, 78]
print(scores[4:])    # Output: [88, 76, 95]
print(scores[::2])   # Output: [85, 78, 88, 95]  (every other item)

Modifying Lists

Updating an Item

Assign a new value to a specific index:

fruits = ["apple", "banana", "cherry"]
fruits[1] = "mango"
print(fruits)  # Output: ['apple', 'mango', 'cherry']

append() — Adding to the End

Add a single item to the end of a list:

scores = [85, 92, 78]
scores.append(90)
print(scores)  # Output: [85, 92, 78, 90]

insert() — Adding at a Specific Position

scores = [85, 92, 78, 90]
scores.insert(1, 100)   # Insert 100 at index 1
print(scores)  # Output: [85, 100, 92, 78, 90]

remove() — Deleting by Value

Remove the first occurrence of a specific value:

fruits = ["apple", "banana", "cherry", "banana"]
fruits.remove("banana")
print(fruits)  # Output: ['apple', 'cherry', 'banana']

pop() — Removing by Index

pop() removes and returns the item at a given index (default: the last item):

scores = [85, 92, 78, 90]
removed = scores.pop()     # Remove last item
print(removed)             # Output: 90
print(scores)              # Output: [85, 92, 78]

scores.pop(0)              # Remove first item
print(scores)              # Output: [92, 78]

Useful List Methods

sort() — Sorting In Place

sort() rearranges the list's items in ascending order by default:

scores = [78, 92, 85, 90, 88]
scores.sort()
print(scores)  # Output: [78, 85, 88, 90, 92]

# Sort in descending order
scores.sort(reverse=True)
print(scores)  # Output: [92, 90, 88, 85, 78]

reverse() — Reversing In Place

fruits = ["apple", "banana", "cherry"]
fruits.reverse()
print(fruits)  # Output: ['cherry', 'banana', 'apple']

Other Helpful Methods

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

print(sum(numbers))           # Output: 36    (total)
print(min(numbers))           # Output: 1     (smallest)
print(max(numbers))           # Output: 9     (largest)
print(numbers.count(1))       # Output: 2     (occurrences of 1)
print(numbers.index(5))       # Output: 4     (first index of 5)

Iterating Through a List

Use a for loop to process each item in a list:

scores = [85, 92, 78, 90, 88]

for score in scores:
    if score >= 90:
        print(f"{score} — Excellent!")
    else:
        print(f"{score} — Good")

Output:

85 — Good
92 — Excellent!
78 — Good
90 — Excellent!
88 — Good

List Comprehensions

A list comprehension is a compact, readable way to build a new list by applying an expression to each item in an existing list. The general syntax is:

new_list = [expression for item in existing_list]

Basic Example

Without a list comprehension:

scores = [85, 92, 78, 90, 88]
curved = []
for score in scores:
    curved.append(score + 5)
print(curved)  # Output: [90, 97, 83, 95, 93]

With a list comprehension — same result, one line:

scores = [85, 92, 78, 90, 88]
curved = [score + 5 for score in scores]
print(curved)  # Output: [90, 97, 83, 95, 93]

With a Condition (Filtering)

Add an if clause to include only items that meet a condition:

scores = [85, 92, 78, 90, 88, 55, 63]
passing = [score for score in scores if score >= 70]
print(passing)  # Output: [85, 92, 78, 90, 88]

Transforming Text Data

names = ["  alice ", "BOB", "  charlie"]
clean_names = [name.strip().title() for name in names]
print(clean_names)  # Output: ['Alice', 'Bob', 'Charlie']

Putting It Together: A Data Science Example

Here's a realistic example using lists to summarize a column of test scores:

scores = [88, 95, 72, 61, 84, 90, 78, 55, 93, 67]

# Summary statistics
print(f"Count:   {len(scores)}")
print(f"Total:   {sum(scores)}")
print(f"Average: {sum(scores) / len(scores):.1f}")
print(f"Min:     {min(scores)}")
print(f"Max:     {max(scores)}")

# Filter passing scores (>= 70)
passing = [s for s in scores if s >= 70]
print(f"Passing: {len(passing)} out of {len(scores)}")

Output:

Count:   10
Total:   783
Average: 78.3
Min:     55
Max:     95
Passing: 7 out of 10

Conclusion

In this lesson, you learned how to create and modify lists using methods like append(), remove(), sort(), and reverse(). You also learned how to access items using indexing and slicing, iterate through lists with loops, and write concise transformations using list comprehensions. Lists are one of the most commonly used data structures in Python and will be a key tool throughout your data science journey. In the next lesson, you'll explore dictionaries, which let you store data as meaningful key-value pairs.

Practice

Coding Assessment

Practice the concepts from this lesson using this Colaboratory notebook. After completing the exercises, save your notebook to GitHub and submit the link for grading.

Knowledge Check

Question 1: What is the index of the first item in a Python list?
Question 2: What does the `append()` method do?
Question 3: What does the following list comprehension produce? 
numbers = [1, 2, 3, 4, 5, 6]
result = [n * 2 for n in numbers if n % 2 == 0]
print(result)