Python list vs. array – when to use?

python

data-structures

performance-optimization

memory-management

byAnton Shumikhin·Mar 3, 2025

Pick a list for versatility and mixed types, best for general use. Choose an array from the array module for numeric data, offering memory efficiency, and acceleration in numerical computation.

# For a buffet-style collection, go with a list
mixed = [42, 'answer', 3.14]  # Contains integers, strings, and floats... oh my!

# For a numerical symphony, choose an array
from array import array
numbers = array('d', [1.1, 2.2, 3.3])  # 'd' for double precision, not for 'doughnut'

Key Points:

Lists are ideal for diversity, mixing up your data types.
Arrays excel in single schematic numeric information, offering both performance and memory perks.

Getting more familiar with list and array

Choosing lists: When versatility trumps all

A list is like a Swiss-Army knife, offering functionality to store elements of mixed types. Python's list is an all-rounder for managing memory efficiently, particularly when the list size would change frequently.

Opt for a list if:

You need a mixed bag of heterogeneous items.
There’s potential resizing, lying in your data’s future. Lists handle growth and shrinkage dynamically.
Simplicity and ease in code maintenance is a priority.

Choosing arrays: Crunching numbers efficiently

The array module is a marathon runner, going the extra mile for performance and space efficiency with large homogeneous data, especially numerical ones.

Choose an `array if:

You’re dealing with purely numeric data of a single type.
You have a vast amount of numerical data and memory efficiency is a pressing concern.
Your Python activities involve C code and require a tight integration.

Distinguishing between processing speed and memory usage

When memory optimization and type safety are crucial

Arrays are leaner than lists as they work without the extra memory space allocated by lists. Hence, arrays are a good call when memory conservation and data type precision trump everything else.

Comparing speed for dynamic changes

While arrays have an upper hand in performance for static-sized data, lists take the cake in executing operations like append and pop swiftly. Basically, lists are the speedy Gonzales of dynamic data sequence.

Sizing responsively with arrays and lists

Dealing with dynamic vs. static collections

Lists are your gladiators in dynamic situations; they expand and contract efficiently, whereas arrays hold fort when the size is static over the program's lifecycle.

Arrays for C extensions or system calls

If your Python code hangs out with C extensions or make a system calls, arrays could be the cool kids on the block as they provide type precision required.