Python Lists with Examples

What is a List in Python

A list in Python is a data structure that represents a modifiable, or alterable, ordered series of elements.

The list is one of four built-in data types in Python that are used to store collections of data; the other three are Tuple, Set, and Dictionary, each of which has distinct characteristics and uses.

Python lists are one of the most useful types of data because they let us work with multiple elements at once. A list can include any number of items and their types can vary (integer, float, string, etc.).

Example:

list1 = ['Prince', 'Grace', 1999, 1998];
list2 = [1, 2, 3, 4, 5 ];
list3 = ["A1", "B2", "C3", "D4"]

The list is a type of data that is mutable. Once a list has been made, elements can be changed, individual values can be changed, and the order of elements can be changed.

A pair of empty square brackets [] or the type constructor list() can be used to create an empty list.

Creating a list in Python is as easy as placing values separated by commas between square brackets.

Accessing Lists in Python Values

Accessing lists in Python values lets you combine the square brackets for slicing with the index or indices to obtain the value accessible at that index.

Example:

list1 = ['Prince', 'Grace', 1999, 1998];
list2 = [1, 2, 3, 4, 5 ];

print (list1[3])
print (list2[1:4])

Output:

1998
[2, 3, 4]

Updating Lists in Python

Updating Lists in Python allows you to update or modify single or multiple list items by placing the slice to the left of the assignment operator.

Example:

list1 = ['Prince', 'Grace', 1999, 1998];
print ("The current value of list[2] is", list1[2])
list1[2] = 2022
print ("The new value of list[2] is", list1[2])

Output:

The current value of list[2] is 1999
The new value of list[2] is 2022

Delete List Elements

To delete list elements, you can use the del statement; otherwise, you can use the remove() method if you know precisely which element(s) you wish to delete.

Example:

list1 = ['Prince', 'Grace', 1999, 1998];
print (list1)
del list1[0];
print ("The list after deleting the index 0:")
print (list1)

Output:

['Prince', 'Grace', 1999, 1998]
The list after deleting the index 0:
['Grace', 1999, 1998]

The remove() is discussed in the List Functions and Methods.

Basic List Operators

The Basic List Operators such as + and * operators behave similarly on lists as they do on strings; they represent concatenation and repetition, but the result is a new list, not a string.

In fact, lists respond to every general sequence operation we used on strings in the previous chapter.

Example:

print(len(['a', 'b', 'c']))
print([1, 2, 3] + [4, 5, 6])
print(['Hey!'] * 4)
print(3 in [1, 2, 3])
for x in [1, 2, 3]: print (x)

Output:

3
[1, 2, 3, 4, 5, 6]
['Hey!', 'Hey!', 'Hey!', 'Hey!']
True
1
2
3

Indexing, Slicing, and Matrixes

Lists in Python are sequences, so the indexing, slicing operator, and matrixes work identically for both lists and strings.

Considering the following input:

Example:

list1 = ['code', 'Code', 'CODE!']

print( list1[2] )
print( list1[-2] )
print( list1[1:] ) 

Output:

CODE!
Code
['Code', 'CODE!']

Python is different from other languages because it lets you use negative indexing.

From the right, you can count the negative indices. Below is an illustration of how the slicing works in List.

List Functions & Methods

Here is the list of Built-in List Functions and Methods in Python.

List Functions

The following list functions are supported by Python:

Example:

list1 = ['Prince', 'Grace', 'Mario', 'Ludy', 'Mc  Quinn']
# cmp is not support in Python 3 but it is still workiin in Python 2
# len
print (len(list1))
# max
print (max(list1))
# min
print (min(list1))


tuple1 = ('Prince', 'Grace', 'Mario', 'Ludy', 'Mc  Quinn')
# list
print (list(tuple1))

Output:

5
Prince
Grace
['Prince', 'Grace', 'Mario', 'Ludy', 'Mc  Quinn']

Note that cmp() is not supported in Python 3.

Python List Methods

The following list methods are supported by Python:

Example:

list1 = ['Prince', 'Grace', "1990", "2000", "2022"]
# append
list1.append("1999")
print(list1)

# count
print(list1.count("2000"))

# extend
list2 = ['banana', 'apple', 'orange']
list1.extend(list2)
print(list1)

# index
print(list1.index("2000"))

# insert
list1.insert(1, "5000")
print(list1)

# pop
print(list1.pop())

# remove
list1.remove("2000")
print(list1)

# reverse
list1.reverse()
print(list1)

# sort
list1.sort()
print(list1)

Output:

['Prince', 'Grace', '1990', '2000', '2022', '1999']
1
['Prince', 'Grace', '1990', '2000', '2022', '1999', 'banana', 'apple', 'orange']
3
['Prince', '5000', 'Grace', '1990', '2000', '2022', '1999', 'banana', 'apple', 'orange']
orange
['Prince', '5000', 'Grace', '1990', '2022', '1999', 'banana', 'apple']
['apple', 'banana', '1999', '2022', '1990', 'Grace', '5000', 'Prince']
['1990', '1999', '2022', '5000', 'Grace', 'Prince', 'apple', 'banana']

What is list comprehension in Python?

List comprehension is a simple and elegant way to make a new list out of an existing list in Python.

An expression followed by a for statement enclosed in square brackets constitutes a list comprehension.

Example: 

sample = [2 ** i for i in range(5)]
print(sample)

Output:

[1, 2, 4, 8, 16]

This code corresponds to:

sample = []
for i in range(5):
   sample.append(2 ** i)

Summary

You’ve learned Python list functions and methods with examples. This lesson addressed accessing values, updating and removing lists, fundamental operations, indexing, slicing, and matrices.

I hope that you’ve learned how to use Python strings from this lesson. If you missed any of our prior lectures, check out our collection of Python Tutorial Topics.

The following post, “Python Tuples,” will instruct you on how to use the built-in functions.

Related Python Tutorials

• What Is Set In Python With Example
• What Is Tuple In Python With Examples
• List Slicing Python With Advanced Examples
• How To Multiply In Python With Examples
• What Is Isdisjoint In Python With Example
• How To Print Words Python With Examples

Common use cases for Python Lists

• Data pipelines. Python is the standard for ETL, data analysis, and ML workflows.
• Web development. Django and FastAPI power modern web backends and APIs.
• Automation and scripting. System administration, file processing, web scraping, and cron jobs.
• Machine learning. scikit-learn, PyTorch, TensorFlow, Hugging Face for AI/ML projects.
• Educational tools. Python’s readability makes it the go-to teaching language.

Working code example

from typing import Optional

def process_data(items: list[dict]) -> Optional[dict]:
    """Process a list of items and return summary stats."""
    if not items:
        return None
    return {
        "count": len(items),
        "total": sum(item.get("value", 0) for item in items),
        "avg": sum(item.get("value", 0) for item in items) / len(items),
    }

# Usage
data = [{"value": 10}, {"value": 20}, {"value": 30}]
summary = process_data(data)
print(summary)  # {'count': 3, 'total': 60, 'avg': 20.0}

Best practices

• Use type hints. list[dict], Optional[str], and TypedDict make code self-documenting and enable static analysis.
• Follow PEP 8. Consistent style improves readability. Use black or ruff to auto-format.
• Prefer f-strings. f”{value}” is cleaner than str.format() or % formatting.
• Write tests with pytest. Aim for 70%+ coverage on business-critical modules.
• Use ruff or pylint. Static analysis catches many bugs before code runs.

Common pitfalls

• Mutable default arguments. def f(x=[]) reuses the same list across calls. Use x=None then check.
• Integer division. 5/2 gives 2.5 in Python 3. Use // for floor division.
• Missing self on methods. Class methods need self as first parameter.
• Late binding closures. Loops that create lambdas can capture variables late.