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array Module Complexity

The array module provides an efficient array type for storing homogeneous data with lower memory overhead than lists.

Complexity Reference

Operation Time Space Notes
array.array() O(n) O(n) Create array
append() O(1) amortized O(1) O(n) worst case when resizing
extend() O(k) O(k) Add multiple
Indexing O(1) O(1) Access by index
Search O(n) O(1) Linear search
Insert O(n) O(1) Insert at position
Remove O(n) O(1) Remove element

Basic Usage

import array

# Create array - O(n)
arr = array.array('i', [1, 2, 3, 4, 5])  # O(5) - integer type

# Type codes: 'i' = int, 'f' = float, 'd' = double, 'b' = byte
arr_float = array.array('f', [1.0, 2.5, 3.14])  # O(3)

# Append - O(1) amortized
arr.append(6)  # O(1)

# Access - O(1)
value = arr[0]  # O(1)

# Length - O(1)
length = len(arr)  # O(1)

Array Operations

Creation Methods

import array

# From iterable - O(n)
arr = array.array('i', range(10))  # O(10)

# From bytes - O(n)
arr = array.array('i')
arr.frombytes(b'\x01\x00\x00\x00\x02\x00\x00\x00')  # O(n)

# From list - O(n)
arr.fromlist([1, 2, 3])  # O(3)

Modification

import array

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

# Insert - O(n)
arr.insert(2, 99)  # O(5) - shift elements

# Remove - O(n)
arr.remove(99)  # O(5) - shift elements

# Pop - O(1) at end, O(n) elsewhere
arr.pop()  # O(1) - remove last
arr.pop(0)  # O(5) - remove first, shift rest

Conversion

import array

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

# To list - O(n)
lst = arr.tolist()  # O(3)

# To bytes - O(n)
bytes_data = arr.tobytes()  # O(3)

# To string - O(n)
str_data = arr.tostring()  # O(3)

Performance Comparison

import array
import sys

# List (flexible type) - O(8) bytes per reference
lst = [1, 2, 3, 4, 5]
lst_size = sys.getsizeof(lst)  # Larger

# Array (homogeneous) - O(4) or O(8) bytes per element
arr = array.array('i', [1, 2, 3, 4, 5])
arr_size = sys.getsizeof(arr)  # Smaller

# Array is more memory efficient for numeric data
print(f"List size: {lst_size}")
print(f"Array size: {arr_size}")

Type Codes

import array

# Available type codes
# 'b' = signed byte (1 byte)
arr_b = array.array('b', [-128, 0, 127])

# 'B' = unsigned byte (1 byte)
arr_B = array.array('B', [0, 128, 255])

# 'i' = signed integer (2-4 bytes)
arr_i = array.array('i', [-1000, 0, 1000])

# 'I' = unsigned integer (2-4 bytes)
arr_I = array.array('I', [0, 1000, 2000])

# 'f' = float (4 bytes)
arr_f = array.array('f', [1.0, 2.5, 3.14])

# 'd' = double (8 bytes)
arr_d = array.array('d', [1.0, 2.5, 3.14])

When to Use Array

Good For:

  • Large collections of numeric data
  • Memory-constrained environments
  • Binary file I/O with numeric data
  • Raw byte operations

Not Good For:

  • Mixed types (use list or tuple)
  • Complex objects (use list)
  • Frequent insertions (use list)
  • Type flexibility needed

Version Notes

  • Python 2.x: array available
  • Python 3.x: Same functionality
  • All versions: O(n) memory savings vs lists
  • list - Flexible container
  • struct - Binary data packing
  • numpy - Numerical arrays (external)

Best Practices

✅ Do:

  • Use for numeric data collections
  • Use for memory efficiency
  • Convert to list for type mixing

❌ Avoid:

  • Mixed types in array
  • Frequent insertions/deletions
  • When flexibility needed