Arrays

The array Module

Python's built-in array module provides space-efficient storage of basic C-like data types:

from array import array

# 'i' = signed int, 'f' = float, 'd' = double, 'u' = unicode char
numbers = array('i', [1, 2, 3, 4, 5])
print(numbers)  # array('i', [1, 2, 3, 4, 5])

Type Codes

Code	C Type	Python Type	Min Size
`'b'`	signed char	int	1 byte
`'B'`	unsigned char	int	1 byte
`'h'`	signed short	int	2 bytes
`'i'`	signed int	int	2-4 bytes
`'f'`	float	float	4 bytes
`'d'`	double	float	8 bytes

Accessing Elements

Arrays support the same indexing and slicing syntax as lists:

arr = array('i', [10, 20, 30, 40, 50])
arr[0]      # 10
arr[-1]     # 50
arr[1:3]    # array('i', [20, 30])
arr[::2]    # array('i', [10, 30, 50])
arr[0] = 99  # mutable: [99, 20, 30, 40, 50]

Looping

arr = array('f', [1.5, 2.5, 3.5])

for val in arr:
    print(val)

for i, val in enumerate(arr):
    print(i, val)

squared = [x ** 2 for x in arr]  # list comprehension

Adding Elements

arr = array('i', [1, 2, 3])

arr.append(4)        # [1, 2, 3, 4]
arr.extend([5, 6])   # [1, 2, 3, 4, 5, 6]
arr.insert(0, 0)     # [0, 1, 2, 3, 4, 5, 6]

Removing Elements

arr = array('i', [1, 2, 3, 4, 5, 3])

arr.pop()       # removes & returns 5
arr.pop(0)      # removes & returns 1
arr.remove(3)   # removes first occurrence of 3
# arr.remove(99)  # ValueError
arr.clear()     # empties array

Sorting and Copying

arr = array('i', [3, 1, 4, 1, 5, 9, 2])

# Arrays don't have a .sort() method
sorted_arr = array('i', sorted(arr))
print(sorted_arr)  # array('i', [1, 1, 2, 3, 4, 5, 9])

# Reverse in place
arr.reverse()
print(arr)

# Copy
shallow = arr[:]       # slice copy
from copy import copy
shallow2 = copy(arr)   # shallow copy
deep = arr.__copy__()  # also shallow

Arrays vs Lists

Feature	array	list
Type constraint	Single type	Any type
Memory	Compact (C-level)	Larger (PyObject*)
Flexibility	Limited methods	Rich methods
Comprehensions	Via list then convert	Built-in
Performance	Faster for homogeneous numeric data	More overhead per element

When to use arrays: Use array when you need to store a large number of numeric values of the same type and memory efficiency matters. For most general-purpose work, lists are more flexible and idiomatic.

Practical Example

from array import array

# Store sensor readings efficiently
readings = array('d', [22.5, 23.1, 21.8, 24.0, 22.9])

mean = sum(readings) / len(readings)
variance = sum((x - mean) ** 2 for x in readings) / len(readings)
std_dev = variance ** 0.5

print(f"Mean: {mean:.2f}, Std Dev: {std_dev:.2f}")

# Write to binary file
with open("readings.bin", "wb") as f:
    readings.tofile(f)

# Read back
restored = array('d')
with open("readings.bin", "rb") as f:
    restored.fromfile(f, 5)

print(restored)  # array('d', [22.5, 23.1, 21.8, 24.0, 22.9])

Note: For heavy numerical work, consider NumPy (numpy.ndarray), which is far more powerful than the built-in array module. The array module is best for simple typed storage without external dependencies.

Practice Exercise

Create an array of 20 random integers between 0 and 100. Write code to: (1) find the minimum, maximum, and average values, (2) create a new array containing only values above the average, (3) remove all odd values from the original array, (4) sort the original array in ascending order using sorted(), and (5) write the sorted array to a binary file and read it back. Compare the memory usage with an equivalent list using sys.getsizeof().