list.append vs Comprehension vs deque: 100k Benchmark

The 2.1x Speed Gap No One Talks About

Most Python tutorials tell you list comprehensions are faster than append() loops. They're right, but they miss the real story: collections.deque can beat both by over 2x for certain insertion patterns, and the performance gap widens as you scale past 10k elements.

I benchmarked three common ways to build a 100,000-element list: manual append(), list comprehension, and deque with conversion. The results expose a fundamental misunderstanding about how Python allocates memory for growing lists. If you've ever built a parser, event buffer, or streaming data handler that progressively accumulates elements, you've probably left performance on the table.

Photo by Markus Spiske on Pexels

The Three Contenders

Here's the setup. Python 3.11, timeit with 100 runs, Intel i7-9700K (your mileage will vary on M1/M2 chips — Apple Silicon's memory subsystem behaves differently).

python
import timeit
from collections import deque

# Method 1: Classic append loop
def build_with_append(n):
    result = []

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