Codectionary / Developer documentation / Python

Lists

A list is Python's built-in ordered, mutable collection type, created with square brackets. Lists can hold items of any type - even a mix of types - and support indexing, slicing, and a wide range of built-in methods for adding, removing, and reordering elements. Because they are mutable, lists can be changed in place after creation, which makes them the go-to structure for collections that grow or shrink over time.

Syntax

[item1, item2, item3]

Examples

Creating and Accessing Lists

Building a list and reading elements by index.

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

print(fruits[0])    # apple
print(fruits[-1])   # cherry (last item)
print(len(fruits))  # 3

Modifying Lists

Adding, inserting, and removing items.

fruits = ["apple", "banana"]

fruits.append("cherry")          # add to the end
fruits.insert(1, "blueberry")    # insert at index 1
print(fruits)  # ['apple', 'blueberry', 'banana', 'cherry']

fruits.remove("banana")          # remove by value
popped = fruits.pop()            # remove and return the last item
print(fruits, popped)

Sorting and Reversing

Ordering list contents in place or as a new list.

numbers = [4, 1, 3, 2]

numbers.sort()               # sorts in place, ascending
print(numbers)                # [1, 2, 3, 4]

numbers.sort(reverse=True)
print(numbers)                # [4, 3, 2, 1]

original = [3, 1, 2]
new_sorted = sorted(original)  # returns a new list, leaves original unchanged
print(original, new_sorted)

List Operations

Combining lists and checking membership.

a = [1, 2, 3]
b = [4, 5]

combined = a + b          # concatenation
print(combined)            # [1, 2, 3, 4, 5]

repeated = [0] * 3
print(repeated)            # [0, 0, 0]

print(3 in a)              # True
print(10 in a)             # False

Best practices

  • Use lists when the collection may grow, shrink, or be reordered; use a tuple instead for fixed, unchanging data
  • Use .append() to add single items and .extend() to add all items from another iterable, not += for large loops
  • Prefer sorted() when you need the original list preserved, and .sort() when modifying in place is fine
  • Avoid using list as a variable name - it shadows the built-in list() type
  • Use list comprehensions instead of building a list with a loop and repeated .append() calls, when the transformation is simple

At a glance

Purpose
Scripting and general-purpose applications
File extension
.py
Runs in
Python interpreter
Usually used with
Python standard library and packages

Specifications & further reading

Related Python documentation

Tuples
A tuple is an ordered, immutable collection, created with parentheses (or often just commas). Once created, a tuple's contents cannot be changed, added to, or removed - this immutability makes tuples faster than lists and safe to use as dictionary keys or in sets. Tuples are commonly used for fixed collections of related values, like coordinates or RGB colors, and for returning multiple values from a function.
Dictionaries
A dictionary stores data as key-value pairs, created with curly braces. Keys must be unique and hashable (strings, numbers, or tuples are common choices), while values can be anything, including other dictionaries or lists. Since Python 3.7, dictionaries maintain insertion order. They are one of the most heavily used data structures in Python, ideal for representing structured records, lookups, and mappings.
Sets
A set is an unordered collection of unique, hashable items, created with curly braces or the set() function. Sets automatically eliminate duplicates and support fast membership testing, along with mathematical set operations like union, intersection, and difference. They're especially useful for deduplicating data and for comparing two collections to find overlaps or differences.
List Comprehensions
A list comprehension is a concise, expressive way to build a new list by applying an expression to every item in an iterable, optionally filtering with a condition. It replaces the common pattern of creating an empty list and appending to it inside a for loop, condensing that logic into a single readable line. Comprehensions are considered idiomatic Python for simple to moderately complex transformations.