About this article
In this article, I’ll explore lists, one of 4 built-in types used to store collections in Python.
This post is the first article in a miniseries exploring the topic of built-in collection types in Python. I based the series on my notes while studying for a Python technical interview.
For quick access, here is the list of posts on this series:
- Python Lists (this article)
- Python Tuples
- Python Sets
- Python Dictionaries
Introduction to Lists
In Python, a list is a data type used for storing a collection of objects. Lists have the following characteristics:
Lists have an order
Lists preserve the order of their elements. Two lists with the same elements but in different order are not equal:
Lists can contain any arbitrary object
List elements can be of any data type. The example below shows a list containing integers, floats, boolean, strings, dictionaries, and other lists:
Lists can contain duplicates
The same element can exist at different indexes in a list. For example, the list below contains the integer 1 at positions 0 and 2:
Lists are mutable
After creating a list, you can change it at will. Lists come with many built-in methods that allow you to modify them. The example below uses the .append() method to add an element to the list:
Lists are dynamic
Lists grow and shrink as needed when adding and removing elements:
Creating a list
You can create a list using a list literal by wrapping its elements in square brackets:
You can also use the list() constructor:
Accessing elements from a list
You can access an element from a list by using the element index. The index indicates the position of the element within the list. Indexes in sequential data types in Python start at 0.
By slicing a list, you can obtain a new list that is a sub-list of the original. The syntax for slicing is:
Which would return a sub-list from index = start to index = (end -1) using the given step (or 1 if no step is given).
Let’s look a some examples.
Slicing with implied step
Slicing with specified step
Slicing with negative step
Slicing from the beginning of the list
Slicing to the end of the list
Slicing with negative indexes
List built-in methods
The .append(x) method adds an item to the end of the list. This method has a constant time complexity of O(1).
The .extend(iterable) method appends all elements in the iterable to the original list. This method has a linear time complexity of O(k), where k is the length of the iterable.
The .insert(i, x) method inserts item x in position i of the list. This method has a linear time complexity of O(n), where n is the length of the list.
The .remove(x) method removes the first element with the value x from the list. It raises a ValueError if x is not in the list. This method has a linear time complexity of O(n), where n is the length of the list.
The .pop(i) method returns the element in position i and removes it from the list. If you don’t pass i to the method, it removes the last element in the list. This method has a constant time complexity O(1) when popping from the end and a linear time complexity O(n) when popping from i, where n is the length of the list.
The .clear() method removes all elements from the list. This method has a linear time complexity of O(n), where n is the length of the list.
The .index(x) method returns the index of the first occurrence of x in the list. This method has a linear time complexity of O(n), where n is the length of the list.
The .count(x) method returns the number of times x appears in the list. This method has a linear time complexity of O(n), where n is the length of the list.
The .sort() method sorts the list in place. This method has a linear-logarithmic time complexity of O(n log n), where n is the length of the list.
The .reverse() method reverses the order of the list in place. This method has a linear time complexity of O(n), where n is the length of the list.
The .copy() method returns a shallow copy of the list. This method has a linear time complexity of O(n), where n is the length of the list.
List comprehensions provide a concise way to create new lists by applying some operation or filter to another sequence or iterable. Let’s look at a few examples.
Creating a list of squares
Say we need a list of the squares of the integers in another list. We could use an iterative solution such as:
Or we could use the following list comprehension, applying the x**2 operation to each element in the original list. The list comprehension is shorter, more readable, and more expressive than the iterative solution:
Creating a list of even numbers
Below is an iterative solution for creating a list of even numbers from another list of numbers:
We can apply a filter to a list comprehension to achieve the same result in a concise way:
In this article, we covered Python lists, a built-in data type you will most likely use in any non-trivial coding project. We covered the main characteristics of lists, the topic of list slicing, the list’s built-in methods with their time complexity, and briefly touched on list comprehension.
I hope that the information covered will help you take advantage of Python lists in your next programming project.
The next article explores Python tuples. See you there!
Python documentation – Data Structures
Real Python – Lists and Tuples in Python