Aprenda Efficient Data Structures — array, deque, bytearray

Deslize para mostrar o menu

Python's built-in list is a general-purpose container that can hold any mix of objects. That flexibility comes at a memory cost. When you know the type and access pattern of your data in advance, more specialized structures save memory and improve performance.

`array.array` — Typed Numeric Arrays

A Python list of integers stores each integer as a full Python object (28+ bytes each). array.array stores raw C-level values – a signed 32-bit integer takes exactly 4 bytes:


              12345678910111213
            
import sys
import array

# Comparing memory usage of list vs array for numeric data
price_list = [100, 200, 150, 300, 250, 400, 175, 225]
price_array = array.array("i", [100, 200, 150, 300, 250, 400, 175, 225])  # "i" = signed int

print(f"list size:  {sys.getsizeof(price_list)} bytes")
print(f"array size: {sys.getsizeof(price_array)} bytes")

# Accessing elements works the same way
print(price_array[0])        # 100
print(price_array[2:5])      # array('i', [150, 300, 250])

Common type codes:

Use array.array when storing large sequences of uniform numeric data without NumPy.

`collections.deque` – Efficient Queue Operations

A list supports fast appends and pops from the right (O(1)), but inserting or removing from the left is O(n) – every element shifts. deque (double-ended queue) supports O(1) operations at both ends:


              1234567891011121314151617181920
            
from collections import deque
import time

# Comparing left-insert performance: list vs deque
size = 100000

list_queue = []
start_time = time.time()
for item_id in range(size):
    list_queue.insert(0, item_id)  # O(n) – slow
list_time = time.time() - start_time

deque_queue = deque()
start_time = time.time()
for item_id in range(size):
    deque_queue.appendleft(item_id)  # O(1) – fast
deque_time = time.time() - start_time

print(f"list insert(0): {list_time:.3f}s")
print(f"deque appendleft: {deque_time:.3f}s")

deque also supports a maxlen parameter – when the deque is full, new items push out the oldest ones automatically. This is ideal for sliding windows and recent-event buffers:


              12345678
            
from collections import deque

# Keeping only the last 5 sensor readings
sensor_readings = deque(maxlen=5)

for reading in [10, 20, 30, 40, 50, 60, 70]:
    sensor_readings.append(reading)
    print(list(sensor_readings))

`bytearray` — Mutable Binary Buffers

bytes is immutable – every modification creates a new object. bytearray is a mutable sequence of bytes, useful for building binary payloads, parsing protocols, or processing binary files in-place:


              12345678910111213
            
import sys

# Building a binary data frame using bytearray
header = bytearray(b"\x01\x02\x03")
payload = bytearray(b"transaction_data")

# Modifying in-place – no new object created
header[0] = 0xFF
print(header)               # bytearray(b'\xff\x02\x03')

# Concatenating efficiently
frame = header + payload
print(sys.getsizeof(frame))

Choosing the Right Structure

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 2. Capítulo 3

Pergunte à IA

Pergunte o que quiser ou experimente uma das perguntas sugeridas para iniciar nosso bate-papo

Seção 2. Capítulo 3

Efficient Data Structures — array, deque, bytearray

array.array — Typed Numeric Arrays

collections.deque – Efficient Queue Operations

bytearray — Mutable Binary Buffers

Choosing the Right Structure

`array.array` — Typed Numeric Arrays

`collections.deque` – Efficient Queue Operations

`bytearray` — Mutable Binary Buffers