`array`

The Python array module provides an efficient data structure for creating arrays of values (often numbers), which are stored more compactly than in standard lists.

Note: The array module isn’t limited strictly to numeric types. While most use cases are for numbers, it also supports Unicode characters with the "w" typecode (added in Python 3.13). The older "u" typecode is deprecated and scheduled for removal in Python 3.16, so prefer "w" in new code.

Arrays can be particularly useful for handling large datasets where memory efficiency is a concern.

Here’s a quick example of an array containing integers:

>>> import array
>>> array.array('i', [1, 2, 3, 4])
array('i', [1, 2, 3, 4])

Key Features

Creates arrays with elements of a specified data type
Offers more memory-efficient storage for homogeneous numeric data compared to lists
Supports all basic operations like slicing, indexing, and iteration

Frequently Used Classes and Functions

Object	Type	Description
`array`	Class	Creates array objects that store items of a specific type
`array.typecode`	Attribute	Returns a character code representing the type of items
`array.append()`	Method	Adds an element to the end of the array
`array.extend()`	Method	Appends items from an iterable
`array.insert()`	Method	Inserts an item in a specific position

Examples

Create an array of integers:

>>> import array

>>> numbers = array.array('i', [1, 2, 3, 4])
>>> numbers
array('i', [1, 2, 3, 4])

Append an element to the array:

>>> numbers.append(5)
>>> numbers
array('i', [1, 2, 3, 4, 5])

Extend the array with elements from an iterable:

>>> numbers.extend([6, 7, 8])
>>> numbers
array('i', [1, 2, 3, 4, 5, 6, 7, 8])

Common Use Cases

Handling large sequences of numbers with less memory overhead
Performing numerical operations on values of homogeneous data types
Interfacing with C libraries that require contiguous data

Real-World Example

Suppose you need to process a large file of integers and calculate their sum efficiently. Here’s how you can do it using the array module. Note that for this code to work, you need an integers.bin file with appropriate content:

>>> import array
>>> import os

>>> # Simulate the binary file
>>> numbers = array.array("i", [10, 20, 30, 40, 50])
>>> with open("integers.bin", "wb") as file:
...     file.write(numbers.tobytes())
...
20

>>> loaded_numbers = array.array("i")
>>> with open("integers.bin", "rb") as file:
...     loaded_numbers.frombytes(file.read())
...

>>> sum(loaded_numbers)
150

By using the array module, the code efficiently reads binary data and computes the sum with minimal memory usage, demonstrating the module’s utility in handling large datasets.

Tutorial

Python's Array: Working With Numeric Data Efficiently

In this tutorial, you'll dive deep into working with numeric arrays in Python, an efficient tool for handling binary data. Along the way, you'll explore low-level data types exposed by the array module, emulate custom types, and even pass a Python array to C for high-performance processing.

intermediate data-structures stdlib

For additional information on related topics, take a look at the following resources: